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High quality images of tunnel surfaces are necessary for visual judgment of abnormal parts. Hence, we propose a monitoring system from a vehicle, which is motion-blur-compensated by the back and forth motion of a galvanometer mirror to offset the vehicle speed, prolong exposure time, and take sharp images including detailed textures. As experimental result of the vehicle-mounted system, we confirmed significant improvements in image quality for a few millimeter-sized ordered black-and-white stripes and cracks, by means of motion blur compensation and prolonged exposure time, under the maximum speed allowed in Japan in a standard tunnel of a highway.

Capturing large-scale outdoor scene by video camera becomes common for various purposes, such as city modeling, surveillance, etc., and demand of recovering high quality image from video data is increasing. Because outdoor scene includes several barriers with multiple depths and motions, e.g.., cars or fences, simply applying motion deblur technique to each frame makes some noise. Furthermore, since color is mixed with foreground and background object near occluding boundary, color separation method during deblurring process is needed to restore the objects. In this paper, we propose a method to recover original boundary of foreground object from multiple blurred input images of video data. By using the refined object boundary, artifact around the border is reduced and accurate deblurring in the whole image is performed. Since both techniques are based on statistical method, quality of recovered image becomes better, if a number of input image increases. Experimental results are shown to prove that our method successfully recovers the deblurred image even if there are severe motion blur and color mixture near occluding boundary.

Restoring blurred image，as one of the hot issues in the field of image processing，has important significance in improving the image quality. In recent years, a variety of methods for removing motion blur of an image have been proposed, but most of the algorithms are too complex and not applied. This paper educes an efficient algorithm for motion-blurred image. According the temporal profile of the infrared detector, point spread function (PFS) of the uniform linear motion-blurred image is discussed. The profile of the PSF is acquired by iterative Weiner-filter. Experimental results show that the method has accurate and applied performance for infrared blurred image got from actual system.

In the field of deep space science detection and high resolution earth observation, a relatively high motion velocity is often generated between the optical camera and the imaging target. Images obtained during the exposure time can produce image motion blur, which becomes one of the main obstacles to acquire high resolution image near the target. As an extended task of the third phase of China’s lunar exploration program, flight imaging of the planned sampling area of Chang’e-5 was carried out. A dual resolution camera with a wide field of view (FOV) camera and a narrow FOV camera was used for imaging mission. High flying speed causes the generation of large motion blurred images captured by the narrow FOV camera and the motion blur can be up to around 30 pixels. To deal with this problem, we analyzed the image features of the blurred images captured by the narrow FOV camera, and proposed a corresponding method that can estimate image motion value from the blurred lunar image based on small craters detection scheme and then adopted the regularization method to restore the image. The algorithm is applied in the batch processing of the real blurred lunar images and has achieved a significant restored effect.

High-speed image acquisition technology has a great significance to improve the effciency of the workpiece surface quality detection, image quality directly affects the final test results. Aiming at the high-speed image acquisition of workpiece surface quality online detection, a workpiece image high-speed online acquisition method was produced. A high-speed online image acquisition sequence was designed. The quantitative relationship between the positioning accuracy in the high speed online image acquisition, motion blur, exposure time and the speed of workpiece was analyzed. The effect between the vibration between transfer mechanism and workpiece was analyzed. Fast trigger was implemented by photoelectric sensor. The accurate positioning was implemented by using the high accuracy time delay module. The motion blur was controlled by reducing the exposure time. A high-speed image acquisition system was designed based on the high-speed image acquisition method. The positioning accuracy was less than 0.1 mm, and the motion blur was less than one pixel.

The goal of infrared image restoration is to reconstruct an original scene from a degraded observation. The restoration process in the application of infrared wavelengths, however, still has numerous research possibilities. In order to give people a comprehensive knowledge of infrared image restoration, the degradation factors divided into two major categories of noise and blur. Many kinds of infrared image restoration method were overviewed. Mathematical background and theoretical basis of infrared image restoration technology, and the limitations or insufficiency of existing methods were discussed. After the survey, the direction and prospects of infrared image restoration technology for the future development were forecast and put forward.

Shift-invariant motion blur can be modeled as a convolution of the true latent image and the blur kernel with additive noise. Blind motion de-blurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. This paper proposes an improved edge-specific motion de-blurring algorithm which proved to be fit for processing remote sensing images. We find that an inaccurate blur kernel is the main factor to the low-quality restored images. To improve image quality, we do the following contributions. For the robust kernel estimation, first, we adapt the multi-scale scheme to make sure that the edge map could be constructed accurately; second, an effective salient edge selection method based on RTV (Relative Total Variation) is used to extract salient structure from texture; third, an alternative iterative method is introduced to perform kernel optimization, in this step, we adopt l1 and l0 norm as the priors to remove noise and ensure the continuity of blur kernel. For the final latent image reconstruction, an improved adaptive deconvolution algorithm based on TV-l2 model is used to recover the latent image; we control the regularization weight adaptively in different region according to the image local characteristics in order to preserve tiny details and eliminate noise and ringing artifacts. Some synthetic remote sensing images are used to test the proposed algorithm, and results demonstrate that the proposed algorithm obtains accurate blur kernel and achieves better de-blurring results.

Motion blur due to camera shaking during exposure is one common phenomena of image degradation. Image motion deblurring is an ill-posed problem, so regularization with image prior and (or) PSF prior is used to estimate PSF and (or) recover original image. In this paper, we exploit image edge prior to estimate PSF based on useful edge selection rule. And we still adopt L1 norm of PSF to ensure its sparsity and Tikhonov regularization to ensure its smoothing during the PSF estimation procedure. And the Laplacian image prior is adopted to restore latent image. The experiment shows that the proposed algorithm outperforms other algorithms.

Recovering a latent image from its blurry version is a severely ill-posed problem. In this paper a post process method is proposed for accurately estimating motion blur kernel based on its prior knowledge. And considering the small details destroy blur kernel estimation, an image decomposition process is executed before the estimation, which can decompose the image into cartoon and texture components. In the iterative framework, the cartoon that contains the main structures will be used to blur kernel estimation for avoiding the artifacts introduced by small texture. In addition, the algorithm adopted in the paper dynamically adjusts the size of patch which contains blur kernel instead of using the fix one as other works. Experimental results show that our method can get the more precise blur kernel and obtain the inspiring deblurring version from single blurry image.

We have developed image processing technologies for resolving issues caused by the inherent UFPA (uncooled focal plane array) sensor characteristics to spread its applications. For example, large time constant of an uncooled IR (infra-red) sensor limits its application field, because motion blur is caused in monitoring the objective moving at high speed. The developed image processing technologies can eliminate the blur and retrieve almost the equivalent image observed in still motion. This image processing is based on the idea that output of the IR sensor is construed as the convolution of radiated IR energy from the objective and impulse response of the IR sensor. With knowledge of the impulse response and moving speed of the objective, the IR energy from the objective can be de-convolved from the observed images. We have successfully retrieved the image without blur using the IR sensor of 15 ms time constant under the conditions in which the objective is moving at the speed of about 10 pixels/60 Hz. The image processing for reducing FPN (fixed pattern noise) has also been developed. UFPA having the responsivity in the narrow wavelength region, e.g., around 8 μm is appropriate for measuring the surface of glass. However, it suffers from severe FPN due to lower sensitivity compared with 8–13 μm. The developed image processing exploits the images of the shutter itself, and can reduce FPN significantly.

Black Sigatoka (BS) is a banana plant disease caused by the fungus Mycosphaerella jiensis . BS symptomscan be observed at late infection stages. By that time, BS has probably spread to other plants. In this paper,we present our current work on building an hyper-spectral (HS) imaging system aimed at in-vivo detection ofBS pre-symptomatic responses in banana leaves. The proposed imaging system comprises a motorized stage, ahigh-sensitivity VIS-NIR camera and an optical spectrograph. To capture images of the banana leaf, the stage'sspeed and camera's frame rate must be computed to reduce motion blur and to obtain the same resolution alongboth spatial dimensions of the resulting HS cube. Our continuous leaf scanning approach allows imaging leavesof arbitrary length with minimum frame loss. Once the images are captured, a denoising step is performed toimprove HS image quality and spectral pro le extraction.Keywords : Hyperspectral Imaging, Black Sigatoka

This study compares the spatial resolution in step-and-shoot and continuous motion acquisition modes of digital tomosynthesis using a bench-top prototype designed for breast phantoms imaging. The prototype employs a flat panel detector with a 50 μm pixel pitch, a micro focus x-ray tube and a motorized stage. A sharp metal edge with a thickness of 0.2 mm was used to measure the modulation transfer function (MTF). The edge was rotated from −7.5° to +7.5° with 1.5° increments to acquire 11 angular projections using 40 kVp, 500 μA with 5.55 s per projection. In continuous motion mode, the motorized stage moved the test object for the whole exposure time at a speed of 0.377 mm/s. The impact of acquisition speed in continuous DBT was also investigated, and a high speed of 0.753 mm/s was used. In step-and-shoot mode, the cutoff frequencies (10% MTF) in projection view (0°) and reconstructed DBT slices were 5.55 lp/mm and 4.95 lp/mm. Spatial resolution dropped in the continuous motion mode of the DBT due to the blur caused by the rotation of the stage and the cutoff frequencies reduced to 3.6 lp/mm and 3.18 lp/mm in the projection view (0o) and reconstructed DBT slices. At high rotational speed in continuous motion mode, the cutoff frequencies in the DBT slices dropped by 17 % to 2.65 lp/mm. Rotational speed of the rotation stage and spatial resolution are interconnected. Hence, reducing the motion blur in the continuous acquisition mode is important to maintain high spatial resolution for diagnostic purposes.

Quantitative computed tomography (CT) applications based on statistical iterative reconstruction algorithms require accurate models of the CT acquisition process, with a key component being the x-ray fan beam intensity. We present a method to experimentally determine the extra-focal radiation profile incident on individual CT detectors. Using a tungsten cylinder as a knife edge, a super-sampled signal was created from sinogram data, which traced the “occlusion” of the x-ray source as seen by a detector. By differentiating this signal and correcting for finite detector size and motion blur, the effective source profile can be recovered. Extra-focal scatter was found to be on the order of 1-3 percent of the focal beam intensity, with lower relative magnitude at the isocenter and increasing towards the edge of the fan beam, with its profile becoming asymmetric at large angles. The implications for reconstruction algorithms and QCT applications will be discussed.

With improving of intelligent and automation in modern industrial production area, the detection and reconstruction of the 3D surface of the product has become an important technology, but the image which acquire on the actual production line has motion blur and this problem will affect the later reconstruction work. In order to solve this problem, a deblurring method which based on double view moving target image is proposed in this paper. We can deduce the relationship of the point spread function(PSF) path between the double view image through the epipolar geometry and the camera model. The experimental results show that deblurring with the PSF path solved by the geometric relationship achieves good results. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

For the real-time motion deblurring, it is of utmost importance to get a higher processing speed with about the same image quality. This paper presents a fast Richardson-Lucy motion deblurring approach to remove motion blur which rotates blurred image under blurring paths. Hence, the computational time is reduced sharply by using one-dimensional Fast Fourier Transform in one-dimensional Richardson-Lucy method. In order to obtain accurate transformational results, interpolation method is incorporated to fetch the gray values. Experiment results demonstrate that the proposed approach is efficient and effective to reduce motion blur under the blur paths.

IC marking provides information about the integrated circuit chips, such as product function and classification. So IC marking inspection is one of the essential processes in semiconductor fabrication. A real-time IC chip marking defect inspection method is presented in this paper. The method comprises the following steps: chip position detection, characters segmentation, feature extraction and classification. The extracted features are used in a back propagation neural network for classifying the types of marking errors such as illegible characters, missing characters and misprinted characters. Character segmentation is an essential part of the inspection method. It is a considerable challenge to segment touching and broken characters correctly, due to uneven illumination, motion blur, as well as problems in the printing process. In order to segment the characters rapidly and accurately, a novel approach for character segmentation based on vertical projection and the character features is proposed. Experiments using a TSSOP20 packaging chip demonstrate that our method can inspect an IC marking with 17 different characters in just 130ms. The system achieves a maximum recognition rate of 98.5%. As a result, it is an ideal solution for a real-time IC marking recognition and defects inspection system.

Images will be blurred by relative motion between the camera and the object of interest. In this paper, we analyzed the process of motion-blurred image, and demonstrated a restoration method based on Lucy-Richardson algorithm. The blur extent and angle can be estimated by Radon transform algorithm and auto-correlation function, respectively, and then the point spread function (PSF) of the motion-blurred image can be obtained. Thus with the help of the obtained PSF, the Lucy-Richardson restoration algorithm is used for experimental analysis on the motion-blurred images that have different blur extents, spatial resolutions and signal-to-noise ratios (SNR’s). Further, its effectiveness is also evaluated by structural similarity (SSIM). Further studies show that, at first, for the image with a spatial frequency of 0.2 per pixel, the modulation transfer function (MTF) of the restored images can maintains above 0.7 when the blur extent is no bigger than 13 pixels. That means the method compensates low frequency information of the image, while attenuates high frequency information. At second, we fund that the method is more effective on condition that the product of the blur extent and spatial frequency is smaller than 3.75. Finally, the Lucy-Richardson algorithm is found insensitive to the Gaussian noise (of which the variance is not bigger than 0.1) by calculating the MTF of the restored image.

The motion blur is one of the common factors leading to blurred images, the parameters of the point spread function (PSF) estimation is the key and prerequisite of motion blurred image restoration. Based on motion blur image characteristics of spectrum and cepstrum analysis, a automatic detection algorithm based on frequency domain and cepstrum domain algorithms is proposed in the paper, which can automatically detect the blur length and blur angle, then we can restorate the motion blur image. Experiments show that when the blur length is 15 ~ 80 pixels noiselessly, In addition to the individual blur length/angle (e.g. 30 pixels/300, 75 pixels/300), blur length estimation error is 0 ~ 0.2 pixels and blur angle estimation error is almost 0. The detection range is greater than some other methods, and the quality of image restoration is good.

We demonstrate an image system with an optical image stabilisation using a droplet manipulation on a liquid crystal (LC) and polymer composite film (LCPCF) to reduce motion blur while preserve image quality. Such an image system adopts a liquid lens on an LCPCF and the mechanism is on a basis of droplet movement on LCPCF whose position changes because electrically tunable orientations of LC molecules on the surface of LCPCF. The change of position of the liquid lens compensates the deviation of light as the image system is under a handshake vibration. As a result, the image system under handshake vibrations could keep a clear image. The operating principles are introduced, and the experiments are performed and discussed. The concept in this paper can also be extended to design other optical components for modulating direction of light.

Existing image registration methods that work in the presence of motion blur assume the scene to be fronto-parallel. In this work, we extend the state-of-the-art by cueing motion blur itself to infer plane inclination. This is achieved by matching extremities of blur kernels computed at different locations in the image. Because these extremities correspond to the same homography, we show that it is possible to find the orientation. Following this, we propose a registration method that reorients the source image to the target plane within a reblur-difference framework to detect the actual changes.

Change detection between two images in the presence of degradations is an important problem in the computer vision community, more so for the aerial scenario which is particularly challenging. Cameras mounted on moving platforms such as aircrafts or drones are subject to general six-dimensional motion as the motion is not restricted to a single plane. With CMOS cameras increasingly in vogue due to their low power consumption, the inevitability of rolling-shutter (RS) effect adds to the challenge. This is caused by sequential exposure of rows in CMOS cameras unlike conventional global shutter cameras where all pixels are exposed simultaneously. The RS effect is particularly pronounced in aerial imaging since each row of the imaging sensor is likely to experience a different motion. For fast-moving platforms, the problem is further compounded since the rows are also affected by motion blur. Moreover, since the two images are shot at different times, illumination differences are common. In this paper, we propose a unified computational framework that elegantly exploits the scarcity constraint to deal with the problem of change detection in images degraded by RS effect, motion blur as well as non-global illumination differences. We formulate an optimization problem where each row of the distorted image is approximated as a weighted sum of the corresponding rows in warped versions of the reference image due to camera motion within the exposure period to account for geometric as well as photometric differences. The method has been validated on both synthetic and real data.

Benefiting from motion blur free, Global shutter pixel is very widely used in the design of CMOS image sensors for high speed applications such as motion vision, scientifically inspection, etc. In global shutter sensors, all pixel signal information needs to be stored in the pixel first and then waiting for readout. For higher frame rate, we need very fast operation of the pixel array. There are basically two ways for the in pixel signal storage, one is in charge domain, such as the one shown in [1], this needs complicated process during the pixel fabrication. The other one is in voltage domain, one example is the one in [2], this pixel is based on the 4T PPD technology and normally the driving of the high capacitive transfer gate limits the speed of the array operation. In this paper we report a new 9T global shutter pixel based on 3-T partially pinned photodiode (PPPD) technology. It incorporates three in-pixel storage capacitors allowing for correlated double sampling (CDS) and pipeline operation of the array (pixel exposure during the readout of the array). Only two control pulses are needed for all the pixels at the end of exposure which allows high speed exposure control.

Motion blur is one of the most significant and common artifacts causing poor image quality in digital photography, in which many factors resulted. In imaging process, if the objects are moving quickly in the scene or the camera moves in the exposure interval, the image of the scene would blur along the direction of relative motion between the camera and the scene, e.g. camera shake, atmospheric turbulence. Recently, sparse representation model has been widely used in signal and image processing, which is an effective method to describe the natural images. In this article, a new deblurring approach based on sparse representation is proposed. An overcomplete dictionary learned from the trained image samples via the KSVD algorithm is designed to represent the latent image. The motion-blur kernel can be treated as a piece-wise smooth function in image domain, whose support is approximately a thin smooth curve, so we employed curvelet to represent the blur kernel. Both of overcomplete dictionary and curvelet system have high sparsity, which improves the robustness to the noise and more satisfies the observer's visual demand. With the two priors, we constructed restoration model of blurred images and succeeded to solve the optimization problem with the help of alternating minimization technique. The experiment results prove the method can preserve the texture of original images and suppress the ring artifacts effectively.

In this paper, we present a method for temporal propagation of depth data that is available for so called key-frames through video sequence. Our method requir es that full frame depth information is assigned. Our method utilizes nearest preceding and nearest following key-frames with known depth information. The propagation of depth information from two sides is essential as it allows to solve most occlusion problems correctly. Image matching is based on the coherency sensitive hashing (CSH) method and is done using image pyramids. Disclosed results are compared with temporal interpolation based on motion vectors from optical flow algorithm. The proposed algorithm keeps sharp depth edges of objects even in situations with fast motion or occlusions. It also handles well many situations, when the depth edges don’t perfectly correspond with true edges of objects. Keywords: 2D to 3D video conversion, video tracking, depth interpolation, patch hashing 1. INTRODUCTION Nowadays, huge gain of interest to 3D stereoscopic video forced multimedia industry to produce more and more of stereo content. This need attracted much attention to conversion techniques, i.e. producing 3D stereo content from common 2D monocular video. Algorithms for creating stereo video from mono stream can be roughly divided in two main subcategories: fully automatic conversion which are implemented most often via specialized chip inside TV, and semi-automatic (operator-assisted) conversion, using special application, tools for marking videos, serious quality control. In this paper, we will focus on the most challenging problem that arises in semi-automatic conversion: temporal propagation of depth data. Operator assisted pipeline for stereo content production usually involves manual drawing of depth for some selected reference frames (key-frames), and subs equent depth propagation for stereo rendering. An initial depth assignment is done sometimes by drawing just disparity scribbles, and after that restored and propagated using 3D cues. In others, full key-frame depth is needed. The bottleneck of this process is quality of propagated depth: if the quality is not high enough, a lot of visually disturbing artifacts appear on final stereo frames. The propagation quality strongly depends on the frequency of manually assigned key -frames, but drawing a lot of frames requires more manual work and makes production slower and more expensive. That’s why the very crucial problem is error-free temporal propagation of depth data through as many as possible frames. Optimal key-frame distance for desired quality of outputs is highly dependent on properties of video sequence and can change significantly within one sequence. 1.1 Prior art The problem of temporal propagation of key-frame depth data is a problematic task. Video data are temporally under-sampled, they contain noise, motion blur and optical effects such as reflections, flares. Moreover objects in the scene can disappear, get occluded or can significantly change shape. The traditional method of depth interpolation uses motion estimation result either on depth or video images. Varenkampf [1 ] proposes to create first estimate of depth by bilateral filtering of previous depth image and then correct the image by estimating motion between depth frames. Similar approach is described in [2]. Harman et al in [3] use machine learning approach for depth assignment for key-frames, which they suggest to select manually, or apply techniques similar for shot-boundary detection. After few points are assigned, a classifier (separate for each key frame) is trai ned using small number of samples, and restore depth in key frame. After that a procedure called “depth tweening” restores intermediate depth frames. For this purpose, both classifiers of neighbor key-frames are fed with image value to produce depth value. For final depth both intermediate depths are weighted by the distance to key frames. Weights could linearly depend on the time distance, but authors propose to use non-linear time-weight dependence. The problem of such approach could arise when intermediate video frame have areas completely different that can be found on key frames (for example, in occlusion areas). However, this situation is difficult for the majority of depth propagation algorithms. Article [14] describes propagation method based on generation of superpixels, matching them and generating depth using matching results and key frames depth.

Simulations of flatbed scanners can shorten the development cycle of new designs, estimate image quality, and lower manufacturing costs. In this paper, we present a flatbed scanner simulation a strobe RGB scanning method that investigates the effect of the sensor height on color artifacts. The image chain model from the remote sensing community was adapted and tailored to fit flatbed scanning applications. This model allows the user to study the relationship between various internal elements of the scanner and the final image quality. Modeled parameters include: sensor height, intensity and duration of illuminant, scanning rate, sensor aperture, detector modulation transfer function (MTF), and motion blur created by the movement of the sensor during the scanning process. These variables are also modeled mathematically by utilizing Fourier analysis, functions that model the physical components, convolutions, sampling theorems, and gamma corrections. Special targets were used to validate the simulation include single frequency pattern, a radial chirp-like pattern, or a high resolution scanned document. The simulation is demonstrated to model the scanning process effectively both on a theoretical and experimental level.

Mean-squared displacement (MSD) analysis is one of the most prevalent tools employed in the application of single-particle tracking to biological systems. In camera-based tracking, the effects of “static error” due to photon fluctuations and “dynamic error” due to motion blur on the MSD have been well-characterized for the case of pure Brownian motion, producing a known constant offset to the straight-line MSD. However, particles tracked in cellular environments often do not undergo pure Brownian motion, but instead can for instance exhibit anomalous diffusion wherein the MSD curve obeys a power law with respect to time, MSD=2D*τα, where D* is an effective diffusion coefficient and 0 < α ≤ 1. There are a number of models that can explain anomalous diffusive behavior in different subcellular contexts. Of these models, fractional Brownian motion (FBM) has been shown to accurately describe the motion of labeled particles such as mRNA and chromosomal loci as they traverse the cytoplasm or nucleoplasm (i.e. crowded viscoelastic environments). Despite the importance of FBM in biological tracking, there has yet to be a complete treatment of the MSD in the presence of static and dynamic errors analogous to the special case of pure Brownian motion. We here present a closed-form, analytical expression of the FBM MSD in the presence of both types of error. We have previously demonstrated its value in live-cell data by applying it to the study of chromosomal locus motion in budding yeast cells. Here we focus on validations in simulated data.

Imaging cells in a microfluidic chamber with an area scan camera is difficult due to motion blur and data loss during frame readout causing discontinuity of data acquisition as cells move at relatively high speeds through the chamber. We have developed a method to continuously acquire high-resolution images of cells in motion through a microfluidics chamber using a high-speed line scan camera. The sensor acquires images in a line-by-line fashion in order to continuously image moving objects without motion blur. The optical setup comprises an epi-illuminated microscope with a 40X oil immersion, 1.4 NA objective and a 150 mm tube lens focused on a microfluidic channel. Samples containing suspended cells fluorescently stained with 0.01% (w/v) proflavine in saline are introduced into the microfluidics chamber via a syringe pump; illumination is provided by a blue LED (455 nm). Images were taken of samples at the focal plane using an ELiiXA+ 8k/4k monochrome line-scan camera at a line rate of up to 40 kHz. The system’s line rate and fluid velocity are tightly controlled to reduce image distortion and are validated using fluorescent microspheres. Image acquisition was controlled via MATLAB’s Image Acquisition toolbox. Data sets comprise discrete images of every detectable cell which may be subsequently mined for morphological statistics and definable features by a custom texture analysis algorithm. This high-throughput screening method, comparable to cell counting by flow cytometry, provided efficient examination including counting, classification, and differentiation of saliva, blood, and cultured human cancer cells.

The electronic rolling shutter mechanism found in many digital cameras may result in spatially-varying blur kernels if camera motion occurs during an imaging exposure. However, existing deblurring algorithms cannot remove the blurs in this case since the blurred image doesn't typically meet the assumptions embedded in these algorithms. This paper attempts to address the problem of modeling and correcting non-uniform image blurs caused by the rolling shutter effect. We introduce a new operator and a mask matrix into the projective motion blur model to describe the blurring process of each row in the image. Based on this modified geometric model, an objective function is formulated and optimized in an alternating scheme. In addition, noisy accelerometer data along x and y directions is incorporated as a regularization term to constrain the solution. The effectiveness of this approach is demonstrated by experimental results on synthetic and real images.

Face images from video sequences captured in unconstrained environments usually contain several kinds of variations, e.g. pose, facial expression, illumination, image resolution and occlusion. Motion blur and compression artifacts also deteriorate recognition performance. Besides, in various practical systems such as law enforcement, video surveillance and e-passport identification, only a single still image per person is enrolled as the gallery set. Many existing methods may fail to work due to variations in face appearances and the limit of available gallery samples. In this paper, we propose a novel approach for still-to-video face recognition in unconstrained environments. By assuming that faces from still images and video frames share the same identity space, a regularized least squares regression method is utilized to tackle the multi-modality problem. Regularization terms based on heuristic assumptions are enrolled to avoid overfitting. In order to deal with the single image per person problem, we exploit face variations learned from training sets to synthesize virtual samples for gallery samples. We adopt a learning algorithm combining both affine/convex hull-based approach and regularizations to match image sets. Experimental results on a real-world dataset consisting of unconstrained video sequences demonstrate that our method outperforms the state-of-the-art methods impressively.

Restoring motion-blurred image is the key technologies in the opto-electronic detection system. The imaging sensors such as CCD and infrared imaging sensor, which are mounted on the motion platforms, quickly move together with the platforms of high speed. As a result, the images become blur. The image degradation will cause great trouble for the succeeding jobs such as objects detection, target recognition and tracking. So the motion-blurred images must be restoration before detecting motion targets in the subsequent images. On the demand of the real weapon task, in order to deal with targets in the complex background, this dissertation uses the new theories in the field of image processing and computer vision to research the new technology of motion deblurring and motion detection. The principle content is as follows: 1) When the prior knowledge about degradation function is unknown, the uniform motion blurred images are restored. At first, the blur parameters, including the motion blur extent and direction of PSF(point spread function), are estimated individually in domain of logarithmic frequency. The direction of PSF is calculated by extracting the central light line of the spectrum, and the extent is computed by minimizing the correction between the fourier spectrum of the blurred image and a detecting function. Moreover, in order to remove the strip in the deblurred image, windows technique is employed in the algorithm, which makes the deblurred image clear. 2) According to the principle of infrared image non-uniform exposure, a new restoration model for infrared blurred images is developed. The fitting of infrared image non-uniform exposure curve is performed by experiment data. The blurred images are restored by the fitting curve.

Non-uniform image blur caused by camera shake or lens aberration is a common degradation in practical capture. Different from the uniform blur, non-uniform one is hard to deal with for its extremely high computation complexity as the blur model computation cannot be accelerated by Fast Fourier Transform (FFT). We propose to compute the most computational consuming operation, i.e. blur model calculation, by an optical computing system to realize fast and accurate non-uniform image deblur. A prototype system composed by a projector-camera system as well as a high dimensional motion platform (for motion blur) or original camera lens (for optics aberrations) is implemented. Our method is applied on a series of experiments, either on synthetic or real captured images, to verify its effectiveness and efficient.

In this paper, we present a method for single image blind deconvolution. Many common forms of blind deconvolution methods need to previously generate a salient image, while the paper presents a novel L0 sparse expression to directly solve the ill-positioned problem. It has no need to filter the blurred image as a restoration step and can use the gradient information as a fidelity term during optimization. The key to blind deconvolution problem is to estimate an accurate kernel. First, based on L2 sparse expression using gradient operator as a prior, the kernel can be estimated roughly and efficiently in the frequency domain. We adopt the multi-scale scheme which can estimate blur kernel from coarser level to finer level. After the estimation of this level’s kernel, L0 sparse representation is employed as the fidelity term during restoration. After derivation, L0 norm can be approximately converted to a sum term and L1 norm term which can be addressed by the Split-Bregman method. By using the estimated blur kernel and the TV deconvolution model, the final restoration image is obtained. Experimental results show that the proposed method is fast and can accurately reconstruct the kernel, especially when the blur is motion blur, defocus blur or the superposition of the two. The restored image is of higher quality than that of some of the art algorithms.

This paper deals with deblurring of aerial imagery and develops a methodology for blind restoration of spatially varying blur induced by camera motion caused by instabilities of the moving platform. This is a topic of significant relevance with a potential impact on image analysis, characterization and exploitation. A sharp image is beneficial not only from the perspective of visual appeal but also because it forms the basis for applications such as moving object tracking, change detection, and robust feature extraction. In the presence of general camera motion, the apparent motion of scene points in the image will vary at different locations resulting in space-variant blurring. However, due to the large distances involved in aerial imaging, we show that the blurred image of the ground plane can be expressed as a weighted average of geometrically warped instances of the original focused but unknown image. The weight corresponding to each warp denotes the fraction of the total exposure duration the camera spent in that pose. Given a single motion blurred aerial observation, we propose a scheme to estimate the original focused image affected by arbitrarily-shaped blur kernels. The latent image and its associated warps are estimated by optimizing suitably derived cost functions with judiciously chosen priors within an alternating minimization framework. Several results are given on the challenging VIRAT aerial dataset for validation.

Current digital breast tomosynthesis (DBT) systems have been shown to have diminished microcalcification (MC) visibility compared to 2D mammography systems. Rotating gantry DBT systems require mechanical motion of the X-ray source which causes motion blurring of the focal spot, thus reducing spatial resolution. We have developed a stationary DBT (s-DBT) technology that uses a carbon nanotube (CNT) based X-ray source array in order to acquire all the projections images without any mechanical motion. It is capable of producing full tomosynthesis datasets with zero motion blur. It has been shown to have significantly higher spatial resolution than continuous motion DBT systems. An s-DBT system also allows for a wider angular span without increasing the acquisition time. A larger angular span covers a larger portion of the Fourier domain, thus decreasing the tissue overlap. In this study, we compare tomosynthesis imaging of MCs, in lumpectomy specimens, between an s-DBT system and a rotating gantry DBT system. Results show that s-DBT produces better MC sharpness and reduced tissue overlap compared to continuous motion DBT systems.

Split-pixel HDR sensor technology is particularly advantageous in automotive applications, because the images are captured simultaneously rather than sequentially, thereby reducing motion blur. However, split pixel technology introduces artifacts in MTF measurement. To achieve a HDR image, raw images are captured from both large and small sub-pixels, and combined to make the HDR output. In some cases, a large sub-pixel is used for long exposure captures, and a small sub-pixel for short exposures, to extend the dynamic range. The relative size of the photosensitive area of the pixel (fill factor) plays a very significant role in the output MTF measurement. Given an identical scene, the MTF will be significantly different, depending on whether you use the large or small sub-pixels i.e. a smaller fill factor (e.g. in the short exposure sub-pixel) will result in higher MTF scores, but significantly greater aliasing. Simulations of split-pixel sensors revealed that, when raw images from both sub-pixels are combined, there is a significant difference in rising edge (i.e. black-to-white transition) and falling edge (white-to-black) reproduction. Experimental results showed a difference of ~50% in measured MTF50 between the falling and rising edges of a slanted edge test chart.

Taking a satisfactory image using a hand-held camera under dim lighting conditions is difficult because there is a tradeoff between exposure time and motion blur. If the image is taken with a short exposure time, the image is noisy, although it preserves sharp details. On the other hand, an image taken with a long exposure time suffers from motion blur but has good intensity. In this paper, we propose a two-image approach that adaptively combines a short-exposure image and a long-exposure image for the image stabilization function on digital cameras. Our method combines only the desirable properties of the two images to obtain a less-blurry and less-noisy image. Furthermore, the proposed approach uses a dual edge-preserving filtering technique for the edge areas of the long-exposure image, which are more affected by motion blur.

We present a novel algorithm that automatically detects anomalies in pipeline right of the way (ROW) regions in aerial surveillance videos. Early detection of anomalies on pipeline ROWs avoids failures or leaks. Vehicles that are potential threats vary in size, shape and color. The detection algorithm should be fast to enable detection in real-time. In this paper, we propose a rotation-invariant gradient histogram based descriptor built in CIELab color space. An SVM with radial basis kernel is used as the classifier. We use only the a and b components since they represent the color values. The region of interest is divided into concentric circular regions. The number of such concentric regions is based on the size of the target. The inner regions capture the local characteristics and finer details of the image. Larger regions capture the global characteristics. A noise reducing differentiation kernel is used to compute the gradient of the region to cope with motion blur and noise introduced by atmospheric aberrations. A gradient orientation histogram is constructed in each region by voting the magnitude of gradients. The final descriptor is build by concatenating the magnitude of DFT of orientation histograms collected from a component and b components. The magnitude of Discrete Fourier Transform (DFT) of the histogram is invariant to rotations. DFT can be efficiently computed as Fast Fourier Transfrom (FFT). Since the algorithm uses a sliding window detector, it can easily be parallelized.

Although High Dynamic Range (HDR) imaging has represented, in the recent years, the topic of important researches, it has not reached yet an excellent level of the HDR scenes ac quisition using the available co mponents. Indeed, many solu-tions have been proposed ranging from bracketing to the beamsp litter but none of these solutions is really consistent with the moving scenes representing light’s level difference. In this paper, we present an optical architecture, which exploits the stereoscopic cameras, ensuring the simultaneous capture of four different ex posures of the same image on four sensors with efficient use of the available light. We also present a short description of the implemented fusion algorithm implemented. Keywords : Stereoscopic cameras, beamsplitters, APEX , HDR Imaging, Cameras architecture, AMP 1. Introduction The extensive development of computer tools is in effervescen ce year by year. It remains on e of the emerging techniques introduced in numerous applications such as quality control, medicine, and video surveillance. The artificial vision tech-niques are adapted to the envisaged applica tions and to the associated constraints. Along with this, the researches in the field of HDR images’ are constantly evolving. Indeed, the process of capturing HDR images has been the work subject of many researchers and hundreds of artists and photographers. As a result, there are many articles and patents describing methods and systems for capturing HDR images. The easiest way to HDR imag-ing involves taking a series of pictures with different exposure times [1, 2]. A lthough this method works well for static scenes, it is unfortunately not well suited for video because of different exposure times for each image, which result in varying amounts of motion blur and other effects occurring after the time of use. However, researchers have extended this approach to video capturing with alternating bright and dark exhibitions [3, 4]. In our case, we will use the stereoscopic technology to generate High Dynamic Range (HDR) images. In order to achieve that, we require three to five images with different exposures. With the combination between two lenses of a camera and a beam splitter, we can get up to four differen t images. Thus, we will study the necessary diaphragm opening allowing us to get the best exposures and define the architecture that we would deploy. The paper will be organized as follow. In the section II, we will discuss about the researches related to stereoscopic cameras and HDR images. Then, Section III will detail the proposed architectur e, the criteria to be met and the results that are expected to get. Finally, th is article will finish by a conclusion and an introduction of some perspectives.

Camera motion blur is a common problem in low-light imaging applications. It is diffcult to apply image restoration techniques without an accurate blur kernel. Recently, inertial sensors have been successfully utilized to estimate the blur function. However, the effectiveness of these restoration algorithms has been limited by lack of access to unprocessed raw image data obtained directly from the Bayer image sensor. In the work, raw CFA image data is acquired in conjunction with 3-axis acceleration data using a custom-built imaging system. The raw image data records the redistribution of light but is effected by camera motion and the rolling shutter mechanism. Through the use of acceleration data, the spread of light to neighboring pixels can be determined. We propose a new approach to jointly perform deblurring and demosaicking of the raw image. This approach adopts edge-preserving sparse prior in a MAP framework. The improvements brought by our algorithm is demonstrated by processing the data collected from the imaging system.

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

Conventional radiography uses a single point x-ray source with a fan or cone beam to visualize various areas of the human body. An imager records the transmitted photons—historically film and now increasingly digital radiography (DR) flat panel detectors—followed by optional image post-processing. Some post-processing techniques of particular interest are tomosynthesis, and dual energy subtraction. Tomosynthesis adds the ability to recreate quasi-3D images from a series of 2D projections. These exposures are typically taken along an arc or other path; and, tomosynthesis reconstruction is used to form a three-dimensional representation of the area of interest. Dual-energy radiography adds the ability to enhance or “eliminate” structures based on their different attenuation of well-separated end-point energies in two exposures. These advanced capabilities come at a high cost in terms of complexity, imaging time, capital equipment, space, and potentially reduced image quality due to motion blur if acquired sequentially. Recently, the prospect of creating x-ray sources, which are composed of arrays of micro-emitters, has been put forward. These arrays offer a flat-panel geometry and may afford advantages in fabrication methodology, size and cost. They also facilitate the use of the dual energy technology. Here we examine the possibility of using such an array of x-ray sources combined with high frame-rate (~kHz) DR detectors to produce advanced medical images without the need for moving gantries or other complex motion systems. Combining the advantages of dual energy imaging with the ability to determine the relative depth location of anatomical structures or pathological findings from imaging procedures should prove to be a powerful diagnostic tool. We also present use cases that would benefit from the capabilities of this modality.

Long-range surveillance systems are typically used in rural areas for detecting and tracking illegal border crossings, trafficking and drug activity. These systems commonly deploy mast or tower-based surveillance systems equipped with thermal infrared cameras, which have the advantage of providing early warnings and increasing the range of observation. However, these systems are subject to high frequency vibration due to slight wind or wind gusts, which is difficult to correct mechanically. In order to identify the border activity, it is critical for the vision system to robustly detect the objects in the scene, classify the objects and track the detected targets. The performance of these post-processing algorithms is known to suffer if the video is not properly stabilized. Surveillance systems in rural areas, particularly in thermal band, pose several unique challenges to video stabilization algorithms. First, the scene rarely contains man-made objects. Water surface, trees and forests present very low contrast and ambiguous textures such that stabilization algorithms struggle to consistently and repeatedly extract distinctive corners and features. Second, even if the system captures certain human activities or structural objects in the scene, the video typically lacks sharpness in the background due to the motion blur at the long range. In this research paper, we propose a biologically-inspired, robust and compact video motion stabilization algorithm, which is ideal for rural areas. Our novel algorithm is compared quantitatively with other competing algorithm (SURF) in terms of robustness and performance. Finally, we evaluate the resource usage on FPGA platforms.

Two of the biggest challenges in designing U×V vision systems are properly representing high dynamic range scene content using low dynamic range components and reducing camera motion blur. SRI’s MASI-HDR (Motion Adaptive Signal Integration-High Dynamic Range) is a novel technique for generating blur-reduced video using multiple captures for each displayed frame while increasing the effective camera dynamic range by four bits or more. MASI-HDR processing thus provides high performance video from rapidly moving platforms in real-world conditions in low latency real time, enabling even the most demanding applications on air, ground and water.

These days there are many real-time 3D measurement systems. Those method has finite exposure time, therefore the motion blur is inevitable in principle. We developed a motion deblurring technique in surface orientation images using a correlation image sensor for 1D movement by belt conveyor. This imaging system consists of two components; one is ring-shaped modulation illumination for encoding surface orientation into the amplitude and phase of the reflected light intensity, and the other is the three-phase correlation image sensor (3PCIS) for demodulating the amplitude and phase of reflected light. The higher spatial frequency components which is lost by motion are captured by modulation imaging using correlation image sensor. The reconstruction algorithm is proposed for modulation imaging picture which is complex value image representing surface orientation. We applied wiener filtering method, and then still normal vector image is successfully reconstructed.

Photo enforcement devices for traffic rules such as red lights, toll, stops, and speed limits are increasingly being deployed in cities and counties around the world to ensure smooth traffic flow and public safety. These are typically unattended fielded systems, and so it is important to periodically check them for potential image/video quality problems that might interfere with their intended functionality. There is interest in automating such checks to reduce the operational overhead and human error involved in manually checking large camera device fleets. Examples of problems affecting such camera devices include exposure issues, focus drifts, obstructions, misalignment, download errors, and motion blur. Furthermore, in some cases, in addition to the sub-algorithms for individual problems, one also has to carefully design the overall algorithm and logic to check for and accurately classifying these individual problems. Some of these issues can occur in tandem or have the potential to be confused for each other by automated algorithms. Examples include camera misalignment that can cause some scene elements to go out of focus for wide-area scenes or download errors that can be misinterpreted as an obstruction. Therefore, the sequence in which the sub-algorithms are utilized is also important. This paper presents an overview of these problems along with no-reference and reduced reference image and video quality solutions to detect and classify such faults.

Laser scanners are essential for scientific research, manufacturing, defense, and medical practice. Unfortunately, often times the speed of conventional laser scanners (e.g., galvanometric mirrors and acousto-optic deflectors) falls short for many applications, resulting in motion blur and failure to capture fast transient information. Here, we present a novel type of laser scanner that offers roughly three orders of magnitude higher scan rates than conventional methods. Our laser scanner, which we refer to as the hybrid dispersion laser scanner, performs inertia-free laser scanning by dispersing a train of broadband pulses both temporally and spatially. More specifically, each broadband pulse is temporally processed by time stretch dispersive Fourier transform and further dispersed into space by one or more diffractive elements such as prisms and gratings. As a proof-of-principle demonstration, we perform 1D line scans at a record high scan rate of 91 MHz and 2D raster scans and 3D volumetric scans at an unprecedented scan rate of 105 kHz. The method holds promise for a broad range of scientific, industrial, and biomedical applications. To show the utility of our method, we demonstrate imaging, nanometer-resolved surface vibrometry, and high-precision flow cytometry with real-time throughput that conventional laser scanners cannot offer due to their low scan rates. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

We suggest a Time-of-Flight (TOF) video camera capturing real-time depth images (a.k.a depth map), which are generated from the fast-modulated IR images utilizing a novel MOEMS modulator having switching speed of 20 MHz. In general, 3 or 4 independent IR (e.g. 850nm) images are required to generate a single frame of depth image. Captured video image of a moving object frequently shows motion drag between sequentially captured IR images, which results in so called ‘motion blur’ problem even when the frame rate of depth image is fast (e.g. 30 to 60 Hz). We propose a novel ‘single shot’ TOF 3D camera architecture generating a single depth image out of synchronized captured IR images. The imaging system constitutes of 2x2 imaging lens array, MOEMS optical shutters (modulator) placed on each lens aperture and a standard CMOS image sensor. The IR light reflected from object is modulated by optical shutters on the apertures of 2x2 lens array and then transmitted images are captured on the image sensor resulting in 2x2 sub-IR images. As a result, the depth image is generated with those simultaneously captured 4 independent sub-IR images, hence the motion blur problem is canceled. The resulting performance is very useful in the applications of 3D camera to a human-machine interaction device such as user interface of TV, monitor, or hand held devices and motion capturing of human body. In addition, we show that the presented 3D camera can be modified to capture color together with depth image simultaneously on ‘single shot’ frame rate.

The basic concept of testing a digital imaging device is to reproduce a known target and to analyze the resulting image. This semi-reference approach can be used for various different aspects of image quality. Each part of the imaging chain can have an influence on the results: lens, sensor, image processing and the target itself. The results are valid only for the complete system. If we want to test a single component, we have to make sure that we change only one and keep all others constant. When testing mobile imaging devices, we run into the problem that hardly anything can be manually controlled by the tester. Manual exposure control is not available for most devices, the focus cannot be influenced and hardly any settings for the image processing are available. Due to the limitations in the hardware, the image pipeline in the digital signal processor (DSP) of mobile imaging devices is a critical part of the image quality evaluation. The processing power of the DSPs allows sharpening, tonal correction and noise reduction to be non-linear and adaptive. This makes it very hard to describe the behavior for an objective image quality evaluation. The image quality is highly influenced by the signal processing for noise and resolution and the processing is the main reason for the loss of low contrast, _ne details, the so called texture blur. We present our experience to describe the image processing in more detail. All standardized test methods use a defined chart and require, that the chart and the camera are not moved in any way during test. In this paper, we present our results investigating the influence of chart movement during the test. Different structures, optimized for different aspects of image quality evaluation, are moved with a defined speed during the capturing process. The chart movement will change the input for the signal processing depending on the speed of the target during the test. The basic theoretical changes in the image will be the introduction of motion blur. With the known speed and the measured exposure time, we can calculate the theoretical motion blur. We compare the theoretical influence of the motion blur with the measured results. We use different methods to evaluate image quality parameter vs. motion speed of the chart. Slanted edges are used to obtain a SFR and to check for image sharpening. The aspect of texture blur is measured using dead leaves structures. The theoretical and measured results are plotted against the speed of the chart and allow an insight into the behavior of the DSP.

Digital cameras under a dark illumination invoke artifacts like motion blur in a long-exposed shot or salient noise corruption in a short-exposed (High ISO) shot. To suppress such artifacts effectively, multi-frame fusion approaches involving the use of multiple short-exposed images has been studied actively. Moreover, it recently has been being applied to various consumer digital cameras for the practical still-shot stabilization. However, it requires too much computational complexities and costs in order to conduct both multiframe noise filtering and brightness/color appearance restoration well from a set of multiple input images acquired at a harsh low-light situation. In this paper, we propose a new fusion-based low-light stabilization approach, which inputs one proper-/long-exposure blurry image as well as multiple short-exposure noisy images. First, a coarse-to-fine motion compensated noise filtering is done to get a clean image from the multiple short-exposure images. Then, online low-light image restoration is followed to obtain a good visual appearance from the denoised image using a blurry long-exposure input image. More specifically, the noise filtering is conducted by a simple block-wise temporal averaging based on a between-frame motion info, which provides a denoising result with even better detail preservation. Our simulation and real scene tests show the possibility of the proposed algorithm for fast and effective low light stabilization at a programmable computing platform.

Although the potential of spectral imaging has been demonstrated in research environments, its adoption by industry has so far been limited due to the lack of high speed, low cost and compact spectral cameras. We have previously presented work to overcome this limitation by monolithically integrating optical interference filters on top of standard CMOS image sensors for high resolution pushbroom hyperspectral cameras. These cameras require a scanning of the scene and therefore introduce operator complexity due to the need for synchronization and alignment of the scanning to the camera. This typically leads to problems with motion blur, reduced SNR in high speed applications and detection latency and overall restricts the types of applications that can use this system. This paper introduces a novel snapshot multispectral imager concept based on optical filters monolithically integrated on top of a standard CMOS image sensor. By using monolithic integration for the dedicated, high quality spectral filters at its core, it enables the use of mass-produced fore-optics, reducing the total system cost. It overcomes the problems mentioned for scanning applications by snapshot acquisition, where an entire multispectral data cube is sensed at one discrete point in time. This is achieved by applying a novel, tiled filter layout and an optical sub-system which simultaneously duplicates the scene onto each filter tile. Through the use of monolithically integrated optical filters it retains the qualities of compactness, low cost and high acquisition speed, differentiating it from other snapshot spectral cameras based on heterogeneously integrated custom optics. Moreover, thanks to a simple cube assembly process, it enables real-time, low-latency operation. Our prototype camera can acquire multispectral image cubes of 256x256 pixels over 32 bands in the spectral range of 600-1000nm at a speed of about 30 cubes per second at daylight conditions up to 340 cubes per second at higher illumination levels as typically used in machine vision applications.