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Image processing in hexagonal lattice has many advantages rather than square lattice. Researchers have addressed benefits of hexagonal structure in applications such as binarization, rotation, scaling and edge detection. Approximately all existing hardwares for capturing and displaying images are based on square lattice. Therefore, the best way for using advantages of hexagonal lattice is to find a proper software approach to convert square pixels to hexagonal ones. This paper presents a hexagonal platform based on interpolation which addresses three existing hexagonal challenges including imperfect hexagonal shape, inaccurate intensity level of hexagonal pixels and lower resolution in hexagonal space. The proposed interpolation is computed by overlaps between square and hexagonal pixels. Overlap types are formulated mathematically in 8 separate cases. Each overlap case is detected automatically and used to compute final gray-level intensity of hexagonal pixels. It is mathematically and experimentally shown that the proposed method satisfies necessary conditions for square-to-hexagonal conversion. The proposed scheme is evaluated on synthetic and real images with 10 different levels of noise in interpolation and edge detection applications. In synthetic images, the proposed method achieves the best figure of merit (FOM) 99.92% and 98.67% in high and low SNRs 100 and 20, respectively. Also, the proposed method outperforms existing state of the art hexagonal lattices with interclass correlation coefficient (ICC) 84.18% and mean rating 7.7 (out of 9) in real images. [ABSTRACT FROM AUTHOR]

Inverse problems are a group of important mathematical problems that aim at estimating source data $x$ and operation parameters $z$ from inadequate observations $y$. In the image processing field, most recent deep learning-based methods simply deal with such problems under a pixel-wise regression framework (from $y$ to $x$) while ignoring the physics behind. In this paper, we re-examine these problems under a different viewpoint and propose a novel framework for solving certain types of inverse problems in image processing. Instead of predicting $x$ directly from $y$ , we train a deep neural network to estimate the degradation parameters $z$ under an adversarial training paradigm. We show that if the degradation behind satisfies some certain assumptions, the solution to the problem can be improved by introducing additional adversarial constraints to the parameter space and the training may not even require pair-wise supervision. In our experiment, we apply our method to a variety of real-world problems, including image denoising, image deraining, image shadow removal, non-uniform illumination correction, and underdetermined blind source separation of images or speech signals. The results on multiple tasks demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to recognizing vanishing-point-constrained building planes from a single image of street view. We first design a novel convolutional neural network (CNN) architecture that generates geometric segmentation of per-pixel orientations from a single street-view image. The network combines two-stream features of general visual cues and surface normals in gated convolution layers, and employs a deeply supervised loss that encapsulates multi-scale convolutional features. Our experiments on a new benchmark with fine-grained plane segmentations of real-world street views show that our network outperforms state-of-the-arts methods of both semantic and geometric segmentation. The pixel-wise segmentation exhibits coarse boundaries and discontinuities. We then propose to rectify the pixel-wise segmentation into perspectively-projected quads based on spatial proximity between the segmentation masks and exterior line segments detected through an image processing. We demonstrate how the results can be utilized to perspectively overlay images and icons on building planes in input photos, and provide visual cues for various applications. [ABSTRACT FROM AUTHOR]

Video object segmentation (VOS) is important for various computer vision problems, and handling it with minimal human supervision is highly desired for the large-scale applications. To bring down the supervision, existing approaches largely follow a data mining perspective by assuming the availability of multiple videos sharing the same object categories. It, however, would be problematic for the tasks that consume a single video. To address this problem, this paper proposes a novel approach that explores weakly labeled images to solve video object segmentation. Given a video labeled with a target category, images labeled with the same category are collected, from which noisy object exemplars are automatically discovered. After that the proposed approach extracts a set of region proposals on various frames and efficiently matches them with massive noisy exemplars in terms of appearance and spatial context. We then jointly select the best proposals across the video by solving a novel submodular problem that combines region voting and global region matching. Finally, the localization results are leveraged as strong supervision to guide pixel-level segmentation. Extensive experiments are conducted on two challenging public databases: Youtube-Objects and DAVIS. The results suggest that the proposed approach improves over previous weakly supervised/unsupervised approaches significantly, showing a performance even comparable with the several approaches supervised by the costly manual segmentations. [ABSTRACT FROM AUTHOR]

The single-feature-based background model often fails in complex scenes, since a pixel is better described by several features, which highlight different characteristics of it. Therefore, the multi-feature-based background model has drawn much attention recently. In this paper, we propose a novel multi-feature-based background model, named stability of adaptive feature (SoAF) model, which utilizes the stabilities of different features in a pixel to adaptively weigh the contributions of these features for foreground detection. We do this mainly due to the fact that the features of pixels in the background are often more stable. In SoAF, a pixel is described by several features and each of these features is depicted by a unimodal model that offers an initial label of the target pixel. Then, we measure the stability of each feature by its histogram statistics over a time sequence and use them as weights to assemble the aforementioned unimodal models to yield the final label. The experiments on some standard benchmarks, which contain the complex scenes, demonstrate that the proposed approach achieves promising performance in comparison with some state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel scheme for scalable image coding based on the concept of epitome. An epitome can be seen as a factorized representation of an image. Focusing on spatial scalability, the enhancement layer of the proposed scheme contains only the epitome of the input image. The pixels of the enhancement layer not contained in the epitome are then restored using two approaches inspired from local learning-based super-resolution methods. In the first method, a locally linear embedding model is learned on base layer patches and then applied to the corresponding epitome patches to reconstruct the enhancement layer. The second approach learns linear mappings between pairs of co-located base layer and epitome patches. Experiments have shown that the significant improvement of the rate-distortion performances can be achieved compared with the Scalable extension of HEVC (SHVC). [ABSTRACT FROM AUTHOR]

This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated by polynomials leading to a polynomial post-nonlinear mixing model. We have shown in a previous paper that the parameters involved in the resulting model can be estimated using least squares methods. A generalized likelihood ratio test based on the estimator of the nonlinearity parameter is proposed to decide whether a pixel of the image results from the commonly used linear mixing model or from a more general nonlinear mixing model. To compute the test statistic associated with the nonlinearity detection, we propose to approximate the variance of the estimated nonlinearity parameter by its constrained Cramér–Rao bound. The performance of the detection strategy is evaluated via simulations conducted on synthetic and real data. More precisely, synthetic data have been generated according to the standard linear mixing model and three nonlinear models from the literature. The real data investigated in this study are extracted from the Cuprite image, which shows that some minerals seem to be nonlinearly mixed in this image. Finally, it is interesting to note that the estimated abundance maps obtained with the post-nonlinear mixing model are in good agreement with results obtained in previous studies. [ABSTRACT FROM PUBLISHER]

In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts-based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the space proximity between image pixels. However, rather than directly using the traditional eigen-based algorithm, we approximate the similarity metric through a deliberately designed kernel function such that pixel values can be explicitly mapped to a high-dimensional feature space. We then apply the conclusion that by appropriately weighting each point in this feature space, the objective functions of the weighted K-means and the normalized cuts share the same optimum points. Consequently, it is possible to optimize the cost function of the normalized cuts by iteratively applying simple K-means clustering in the proposed feature space. LSC possesses linear computational complexity and high memory efficiency, since it avoids both the decomposition of the affinity matrix and the generation of the large kernel matrix. By utilizing the underlying mathematical equivalence between the two types of seemingly different methods, LSC successfully preserves global image structures through efficient local operations. Experimental results show that LSC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms in terms of several commonly used evaluation metrics in image segmentation. The applicability of LSC is further demonstrated in two related computer vision tasks. [ABSTRACT FROM AUTHOR]

According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often cannot completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection. Our key observation is from that each image pixel lies along a color line in normalized RGB space and the different color lines representing distinct diffuse chromaticities intersect at one point, namely, the illumination chromaticity. For pixels along the same color line, they spread over the entire image and their distances to the illumination chromaticity reflect the amount of specular reflection components. With global (non-local) information from these color lines, our method can effectively separate specular and diffuse reflection components in a pixelwise way for a single image, and it is suitable for real-time applications. Our experimental results on synthetic and real images show that our method performs better than the state-of-the-art methods to separate specular reflection. [ABSTRACT FROM PUBLISHER]

In this paper, we are concerned with image downsampling using subpixel techniques to achieve superior sharpness for small liquid crystal displays (LCDs). Such a problem exists when a high-resolution image or video is to be displayed on low-resolution display terminals. Limited by the low-resolution display, we have to shrink the image. Signal-processing theory tells us that optimal decimation requires low-pass filtering with a suitable cutoff frequency, followed by downsampling. In doing so, we need to remove many useful image details causing blurring. Subpixel-based downsampling, taking advantage of the fact that each pixel on a color LCD is actually composed of individual red, green, and blue subpixel stripes, can provide apparent higher resolution. In this paper, we use frequency-domain analysis to explain what happens in subpixel-based downsampling and why it is possible to achieve a higher apparent resolution. According to our frequency-domain analysis and observation, the cutoff frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using a novel antialiasing filter. Applying the proposed filters to two existing subpixel downsampling schemes called direct subpixel-based downsampling (DSD) and diagonal DSD (DDSD), we obtain two improved schemes, i.e., DSD based on frequency-domain analysis (DSD-FA) and DDSD based on frequency-domain analysis (DDSD-FA). Experimental results verify that the proposed DSD-FA and DDSD-FA can provide superior results, compared with existing subpixel or pixel-based downsampling methods. [ABSTRACT FROM AUTHOR]

Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades et al, which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case. [ABSTRACT FROM AUTHOR]

Designing effective image priors is of great interest to image super-resolution (SR), which is a severely under-determined problem. An edge smoothness prior is favored since it is able to suppress the jagged edge artifact effectively. However, for soft image edges with gradual intensity transitions, it is generally difficult to obtain analytical forms for evaluating their smoothness. This paper characterizes soft edge smoothness based on a novel SoftCuts metric by generalizing the Geocuts method [1]. The proposed soft edge smoothness measure can approximate the average length of all level lines in an intensity image. Thus, the total length of all level lines can be minimized effectively by integrating this new form of prior. In addition, this paper presents a novel combination of this soft edge smoothness prior and the alpha matting technique for color image SR, by adaptively normalizing image edges according to their α-channel description. This leads to the adaptive SoftCuts algorithm, which represents a unified treatment of edges with different contrasts and scales. Experimental results are presented which demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we closely scrutinize the spatial and spectral properties of aperiodic halftoning schemes on rectangular and hexagonal sampling grids. Traditionally, hexagonal sampling grids have been shunned due to their inability to preserve the high-frequency components of blue-noise dither patterns at gray-levels near one-half, but as will be shown, only through the introduction of diagonal correlations between dots can even rectangular sampling grids preserve these frequencies. And by allowing the sampling grid to constrain the placement of dots, a particular algorithm may introduce visual artifacts just as disturbing as excess energy below the principal frequency. If, instead, the algorithm maintains radial symmetry by introducing a minimum degree of clustering, then that algorithm can maintain its grid defiance illusion fundamental to the spirit of the blue-noise model. As such, this paper shows that hexagonal grids are preferrable because they can support gray-levels near one-half with less required clustering of minority pixels and a higher principal frequency. Along with a thorough Fourier analysis of blue-noise dither patterns on both rectangular and hexagonal sampling grids, this paper also demonstrates the construction of a blue-noise dither array for hexagonal grids. EDICS: 4-QUAN Quantization and Halftoning. [ABSTRACT FROM AUTHOR]

Subpixel-based image scaling can improve the apparent resolution of displayed images by controlling individual subpixels rather than whole pixels. However, improved luminance resolution brings chrominance distortion, making it crucial to suppress color error while maintaining sharpness. Moreover, it is challenging to develop a scheme that is applicable for various subpixel arrangements and for arbitrary scaling factors. In this paper, we address the aforementioned issues by proposing a generalized continuous-domain analysis model, which considers the low-pass nature of the human visual system (HVS). Specifically, given a discrete image and a grid-like subpixel arrangement, the signal perceived by the HVS is modeled as a 2D continuous image. Minimizing the difference between the perceived image and the continuous target image leads to the proposed scheme, which we call continuous-domain analysis for subpixel-based scaling (CASS). To eliminate the ringing artifacts caused by the ideal low-pass filtering in CASS, we propose an improved scheme, which we call CASS with Laplacian-of-Gaussian filtering. Experiments show that the proposed methods provide sharp images with negligible color fringing artifacts. Our methods are comparable with the state-of-the-art methods when applied on the RGB stripe arrangement, and outperform existing methods when applied on other subpixel arrangements. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function, which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on the opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm, which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified median-cut method. Compared with previous approaches, our method can produce high-quality results with a fewer visual artifacts but also requires significantly less computational effort. [ABSTRACT FROM AUTHOR]

Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly important. In this paper, we propose a unified framework called pixelwise image saliency aggregating (PISA) various bottom-up cues and priors. It generates spatially coherent yet detail-preserving, pixel-accurate, and fine-grained saliency, and overcomes the limitations of previous methods, which use homogeneous superpixel based and color only treatment. PISA aggregates multiple saliency cues in a global context, such as complementary color and structure contrast measures, with their spatial priors in the image domain. The saliency confidence is further jointly modeled with a neighborhood consistence constraint into an energy minimization formulation, in which each pixel will be evaluated with multiple hypothetical saliency levels. Instead of using global discrete optimization methods, we employ the cost-volume filtering technique to solve our formulation, assigning the saliency levels smoothly while preserving the edge-aware structure details. In addition, a faster version of PISA is developed using a gradient-driven image subsampling strategy to greatly improve the runtime efficiency while keeping comparable detection accuracy. Extensive experiments on a number of public data sets suggest that PISA convincingly outperforms other state-of-the-art approaches. In addition, with this work, we also create a new data set containing 800 commodity images for evaluating saliency detection. [ABSTRACT FROM PUBLISHER]

This paper advocates a novel multiscale, structure-sensitive saliency detection method, which can distinguish multilevel, reliable saliency from various natural pictures in a robust and versatile way. One key challenge for saliency detection is to guarantee the entire salient object being characterized differently from nonsalient background. To tackle this, our strategy is to design a structure-aware descriptor based on the intrinsic biharmonic distance metric. One benefit of introducing this descriptor is its ability to simultaneously integrate local and global structure information, which is extremely valuable for separating the salient object from nonsalient background in a multiscale sense. Upon devising such powerful shape descriptor, the remaining challenge is to capture the saliency to make sure that salient subparts actually stand out among all possible candidates. Toward this goal, we conduct multilevel low-rank and sparse analysis in the intrinsic feature space spanned by the shape descriptors defined on over-segmented super-pixels. Since the low-rank property emphasizes much more on stronger similarities among super-pixels, we naturally obtain a scale space along the rank dimension in this way. Multiscale saliency can be obtained by simply computing differences among the low-rank components across the rank scale. We conduct extensive experiments on some public benchmarks, and make comprehensive, quantitative evaluation between our method and existing state-of-the-art techniques. All the results demonstrate the superiority of our method in accuracy, reliability, robustness, and versatility. [ABSTRACT FROM AUTHOR]

It is very challenging to synthesize a high dynamic range (HDR) image from multiple differently exposed low dynamic range images when there are moving objects in the images. This is due to the fact that the moving objects will cause ghosting artifacts to appear in the synthesized HDR image. To prevent such artifacts, a patching algorithm is required to correct motion regions such that all the moving objects are synchronized in the differently exposed images. In this paper, a new optimization problem is formulated to correct the motion regions of the multiple differently exposed images by considering both spatial and temporal consistencies. The resultant scheme is a hybrid patching scheme composed of a correction method which is an intensity mapping function at pixel level, and a hole-filling method that uses block-level template matching. The proposed patching scheme is not only robust to large intensity changes in these input images, but also at regions that are over-or under-exposed. Experimental results show that the proposed method is able to prevent ghosting artifacts from appearing in the final synthesized HDR image. [ABSTRACT FROM AUTHOR]

Color sampling based matting methods find the best known samples for foreground and background colors of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Furthermore, current sampling based matting methods choose samples that are located around the boundaries of foreground and background regions. In this paper, we overcome these two problems. First, we propose texture as a feature that can complement color to improve matting by discriminating between known regions with similar colors. The contribution of texture and color is automatically estimated by analyzing the content of the image. Second, we combine local sampling with a global sampling scheme that prevents true foreground or background samples to be missed during the sample collection stage. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results shows that the proposed method is ranked first among all other image matting methods. [ABSTRACT FROM AUTHOR]

In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45^\circ and 135^\circ CDMs for interpolating the diagonal pixels and 2) the 0^\circ and 90^\circ CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications. [ABSTRACT FROM AUTHOR]

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm. [ABSTRACT FROM PUBLISHER]

Infrared focal-plane array (IRFPA) detectors suffer from fixed-pattern noise (FPN) that degrades image quality, which is also known as spatial nonuniformity. FPN is still a serious problem, despite recent advances in IRFPA technology. This paper proposes new scene-based correction algorithms for continuous compensation of bias and gain nonuniformity in FPA sensors. The proposed schemes use recursive least-square and affine projection techniques that jointly compensate for both the bias and gain of each image pixel, presenting rapid convergence and robustness to noise. The synthetic and real IRFPA videos experimentally show that the proposed solutions are competitive with the state-of-the-art in FPN reduction, by presenting recovered images with higher fidelity. [ABSTRACT FROM AUTHOR]

The problem we will consider in this paper is binary image restoration. It is, in essence, difficult to solve because of the combinatorial nature of the problem. To overcome this difficulty, we propose a new minimization model by making use of a new variable to enforce the image to be binary. Based on the proposed minimization model, we present a fast alternating minimization algorithm for binary image restoration. We prove the convergence of the proposed alternating minimization algorithm. Experimental results show that the proposed method is feasible and effective for binary image restoration. [ABSTRACT FROM AUTHOR]

Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology. [ABSTRACT FROM PUBLISHER]

This paper proposes a low-distortion transform for prediction-error expansion reversible watermarking. The transform is derived by taking a simple linear predictor and by embedding the expanded prediction error not only into the current pixel but also into its prediction context. The embedding ensures the minimization of the square error introduced by the watermarking. The proposed transform introduces less distortion than the classical prediction-error expansion for complex predictors such as the median edge detector or the gradient-adjusted predictor. Reversible watermarking algorithms based on the proposed transform are analyzed. Experimental results are provided. [ABSTRACT FROM PUBLISHER]

Smile detection in face images captured in unconstrained real-world scenarios is an interesting problem with many potential applications. This paper presents an efficient approach to smile detection, in which the intensity differences between pixels in the grayscale face images are used as features. We adopt AdaBoost to choose and combine weak classifiers based on intensity differences to form a strong classifier. Experiments show that our approach has similar accuracy to the state-of-the-art method but is significantly faster. Our approach provides 85% accuracy by examining 20 pairs of pixels and 88% accuracy with 100 pairs of pixels. We match the accuracy of the Gabor-feature-based support vector machine using as few as 350 pairs of pixels. [ABSTRACT FROM PUBLISHER]

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters. [ABSTRACT FROM PUBLISHER]

A multiple exposure fusion to enhance the dynamic range of an image is proposed. The construction of high dynamic range images (HDRIs) is performed by combining multiple images taken with different exposures and estimating the irradiance value for each pixel. This is a common process for HDRI acquisition. During this process, displacements of the images caused by object movements often yield motion blur and ghosting artifacts. To address the problem, this paper presents an efficient and accurate multiple exposure fusion technique for the HDRI acquisition. Our method simultaneously estimates displacements and occlusion and saturation regions by using maximum a posteriori estimation and constructs motion-blur-free HDRIs. We also propose a new weighting scheme for the multiple image fusion. We demonstrate that our HDRI acquisition algorithm is accurate, even for images with large motion. [ABSTRACT FROM PUBLISHER]

A power-constrained contrast-enhancement algorithm for emissive displays based on histogram equalization (HE) is proposed in this paper. We first propose a log-based histogram modification scheme to reduce overstretching artifacts of the conventional HE technique. Then, we develop a power-consumption model for emissive displays and formulate an objective function that consists of the histogram-equalizing term and the power term. By minimizing the objective function based on the convex optimization theory, the proposed algorithm achieves contrast enhancement and power saving simultaneously. Moreover, we extend the proposed algorithm to enhance video sequences, as well as still images. Simulation results demonstrate that the proposed algorithm can reduce power consumption significantly while improving image contrast and perceptual quality. [ABSTRACT FROM PUBLISHER]

It is difficult to directly apply existing binarization approaches to the barcode images captured by mobile device due to their low quality. This paper proposes a novel scheme for the binarization of such images. The barcode and background regions are differentiated by the number of edge pixels in a search window. Unlike existing approaches that center the pixel to be binarized with a window of fixed size, we propose to shift the window center to the nearest edge pixel so that the balance of the number of object and background pixels can be achieved. The window size is adaptive either to the minimum distance to edges or minimum element width in the barcode. The threshold is calculated using the statistics in the window. Our proposed method has demonstrated its capability in handling the nonuniform illumination problem and the size variation of objects. Experimental results conducted on 350 images captured by five mobile phones achieve about 100% of recognition rate in good lighting conditions, and about 95% and 83% in bad lighting conditions. Comparisons made with nine existing binarization methods demonstrate the advancement of our proposed scheme. [ABSTRACT FROM PUBLISHER]

It is well known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not scale with the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image-processing literature. Along with the spatial filter, the edge-preserving bilateral filter involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process nonlinear and computationally intensive, particularly when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant time, by using trigonometric range kernels. This is done by generalizing the idea presented by Porikli, i.e., using polynomial kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained by Porikli using polynomials. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally. [ABSTRACT FROM AUTHOR]

The classic centroidal Voronoi tessellation (CVT) model and its generalizations work quite well at extracting uniformly colored objects, but often fail to handle images with distinct color distribution or strong inhomogeneous intensity. To resolve this problem within the CVT methodology, in this paper we incorporate the information of local variation of colors/intensities and the length of boundaries into the energy functional and develop a new model called the Local Variation and Edge-Weighted Centroidal Voronoi Tessellation (LVEWCVT) for image segmentation. Its mathematical formulation and practical implementations are also discussed and given. We test the LVEWCVT method on various type of segments and also compare it with several state-of-art algorithms using extensive segmentation examples, the results demonstrate excellent performance and competence of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel learning-based face hallucination framework built in the DCT domain, which can produce a high-resolution face image from a single low-resolution one. The problem is formulated as inferring the DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Our study shows that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. A simple but effective learning-based inference model is proposed to infer the ac coefficients. Experiments have been conducted to demonstrate the effectiveness of the proposed method in producing high quality hallucinated face images. [ABSTRACT FROM AUTHOR]

2-D phase unwrapping is an important technique in many applications. However, with the growth of image scale, how to tile and splice the image effectively has become a new challenge. In this paper, the phase unwrapping problem is abstracted as solving a large-scale system of inconsistent linear equations. With the difficulties of large-scale phase unwrapping analyzed, L^0-norm criterion is found to have potentials in efficient image tiling and splicing. Making use of the clustering characteristic of residue distribution, a tiling strategy is proposed for L^0-norm criterion. Unfortunately, L^0-norm is an NP-hard problem, which is very difficult to find an exact solution in a polynomial time. In order to effectively solve this problem, equations corresponding to branch cuts of L^0-norm in the inconsistent equation system mentioned earlier are considered as outliers, and then an outlier-detection-based phase unwrapping method is proposed. Through this method, a highly accurate approximate solution to this NP-hard problem is achieved. A set of experimental results shows that the proposed approach can avoid the inconsistency between local and global phase unwrapping solutions caused by image tiling. [ABSTRACT FROM AUTHOR]

The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as “single-image super-resolution,” is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI) refid="ref11"/. The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to contour approximation carried out by means of the B-snake algorithm and the dynamic programming (DP) optimization technique. Using the proposed strategy for contour point search procedure, computing complexity is reduced to O(N\times M^2), whereas the standard DP method has an O(N\times M^4) complexity, with N being the number of contour sample points and M being the number of candidates in the search space. The storage requirement was also decreased from N\times M^3 to N\times M memory elements. Some experiments on noise corrupted synthetic image, magnetic resonance, and computer tomography medical images have shown that the proposed approach results are equivalent to those obtained by the standard DP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a fast implementation of the bilateral filter with arbitrary range and domain kernels. It is based on the histogram-based fast bilateral filter approximation that uses uniform box as the domain kernel. Instead of using a single box kernel, multiple box kernels are used and optimally combined to approximate an arbitrary domain kernel. The method achieves better approximation of the bilateral filter compared to the single box kernel version with little increase in computational complexity. We also derive the optimal kernel size when a single box kernel is used. [ABSTRACT FROM PUBLISHER]

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments. [ABSTRACT FROM PUBLISHER]

Otsu's binarization method is one of the most popular image-thresholding methods; Student's t-test is one of the most widely-used statistical tests to compare two groups. This paper aims to stress the equivalence between Otsu's binarization method and the search for an optimal threshold that provides the largest absolute Student's t-statistic. It is then naturally demonstrated that the extension of Otsu's binarization method to multi-level thresholding is equivalent to the search for optimal thresholds that provide the largest F-statistic through one-way analysis of variance (ANOVA). Furthermore, general equivalences between some parametric image-thresholding methods and the search for optimal thresholds with the largest likelihood-ratio test statistics are briefly discussed. [ABSTRACT FROM PUBLISHER]

This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the \ell^1 fidelity term and the Mumford–Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the \ell^1 and \ell^2 norms. The regularizer in the functional is formed by the \ell^1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed. [ABSTRACT FROM AUTHOR]

Human observers understand the content of an image intuitively. Based upon image content, they perform many image-related tasks, such as creating slide shows and photo albums, and organizing their image archives. For example, to select photos for an album, people assess image quality based upon the main objects in the image. They modify colors in an image based upon the color of important objects, such as sky, grass or skin. Serious photographers might modify each object separately. Photo applications, in contrast, use low-level descriptors to guide similar tasks. Typical descriptors, such as color histograms, noise level, JPEG artifacts and overall sharpness, can guide an imaging application and safeguard against blunders. However, there is a gap between the outcome of such operations and the same task performed by a person. We believe that the gap can be bridged by automatically understanding the content of the image. This paper presents algorithms for automatic tagging of perceptual objects in images, including sky, skin, and foliage, which constitutes an important step toward this goal. [ABSTRACT FROM AUTHOR]

Differentiating region-of-interest (ROI) from non-ROI in an image in terms of relative size as well as fidelity becomes an important functionality for future visual communication environment with a variety of display devices. In this paper, we propose a scalable image representation with the ROI functionality in the spatial domain, which allows us to generate a hierarchy of images with arbitrary sizes. The ROI functionality of our scalable representation is a result of a nonuniform grid transformation in the spatial domain, where only the center of ROI and an expansion parameter are to be known. Our grid transformation guarantees no loss of information within the area of ROI. [ABSTRACT FROM PUBLISHER]

Both image registration and fusion can be formulated as estimation problems. Instead of estimating the registration parameters and the true scene separately as in the conventional way, we propose a maximum likelihood approach for joint image registration and fusion in this paper. More precisely, the fusion performance is used as the criteria to evaluate the registration accuracy. Hence, the registration parameters can be automatically tuned so that both fusion and registration can be optimized simultaneously. The expectation maximization algorithm is employed to solve this joint optimization problem. The Cramer-Rao bound (CRB) is then derived. Our experiments use several types of sensory images for performance evaluation, such as visual images, IR thermal images, and hyperspectral images. It is shown that the mean square error of estimating the registration parameters using the proposed method is close to the CRBs. At the mean time, an improved fusion performance can be achieved in terms of the edge preservation measure Q^AB/F, compared to the Laplacian pyramid fusion approach. [ABSTRACT FROM PUBLISHER]

In a resolution scalable image coding algorithm, a multiresolution representation of the data is often obtained using a linear filter bank. Reversible cellular automata have been recently proposed as simpler, nonlinear filter banks that produce a similar representation. The original image is decomposed into four subbands, such that one of them retains most of the features of the original image at a reduced scale. In this paper, we discuss the utilization of reversible cellular automata and arithmetic coding for scalable compression of binary and grayscale images. In the binary case, the proposed algorithm that uses simple local rules compares well with the JBIG compression standard, in particular for images where the foreground is made of a simple connected region. For complex images, more efficient local rules based upon the lifting principle have been designed. They provide compression performances very close to or even better than JBIG, depending upon the image characteristics. In the grayscale case, and in particular for smooth images such as depth maps, the proposed algorithm outperforms both the JBIG and the JPEG2000 standards under most coding conditions. [ABSTRACT FROM PUBLISHER]

In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD). [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel framework that exploits an informative reference channel in the processing of another channel. We formulate the problem as a maximum a posteriori estimation problem considering a reference channel and develop a probabilistic model encoding the interchannel correlations based on Markov random fields. Interestingly, the proposed formulation results in an image-specific and region-specific linear filter for each site. The strength of filter response can also be controlled in order to transfer the structural information of a channel to the others. Experimental results on satellite image fusion and chrominance image interpolation with denoising show that our method provides improved subjective and objective performance compared with conventional approaches. [ABSTRACT FROM AUTHOR]

A number of algorithms have been proposed for intelligent image/video retargeting with image content retained as much as possible. However, they usually suffer from some artifacts in the results, such as ridge or structure twist. In this paper, we present a structure-preserving media retargeting technique that preserves the content and image structure as best as possible. Different from the previous pixel or grid based methods, we estimate the image content saliency from the structure of the content. A block structure energy is introduced with a top-down strategy to constrain the image structure inside to deform uniformly in either x or y direction. However, the flexibilities for retargeting are quite different for different images. To cope with this problem, we propose a compressibility assessment scheme for media retargeting by combining the entropies of image gradient magnitude and orientation distributions. Thus, the resized media is produced to preserve the image content and structure as best as possible. Our experiments demonstrate that the proposed method provides resized images/videos with better preservation of content and structure than those by the previous methods. [ABSTRACT FROM AUTHOR]