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Vignetting is a phenomenon characterized by a decrease in illumination towards the edges of an image. This effect is typically represented by a radially symmetrical model, however, this paper aims to demonstrate a non-radial model of vignetting and estimate its shape. To accomplish this, a synthetic image was created and the angular vignetting shape has been modeled as a sum of harmonics. The magnitudes and amplitudes of these harmonics were obtained and used to construct the desired angular vignetting shape. Once the synthetic image with the modeled vignetting shape and added noise was created, it was used as input into a function for vignetting estimation. Also, the inputs have been a fixed vignetting center and different initial values of harmonics’ magnitudes and phases. With that inputs, despite the level of the noise, we have successfully estimated vignetting function by non-linear optimization. The function has attempted to determine the original harmonics’ used to create the vignetting angular shape. When the vignetting model is calculated, we removed it in order to get a homogeneous image. While it may be difficult to obtain the exact original values of the harmonics’, the shape can be estimated with a high level of accuracy. The paper shows that highly accurate models can be estimated for a lower number of angular harmonics, with a residual gain error standard deviation of less than 0.03%. Even in the presence of 5dB noise in the images, the gain error standard deviation remains below 3%, as long as proper parameter initialization is performed prior to optimization.

Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

Moire patterns, mainly due to the aliasing between the grids of display devices and camera sensors, often occur in recaptured screen images (videos). The spatial variety in densities and scales makes it difficult to remove moire patterns with hand-crafted priors. Prevalent supervised-learning based methods require huge amount of paired images, which are difficult to capture and align. This paper proposes an unsupervised Generative Adversarial Network for Moire Removal (MR-GAN), which is the first attempt at unsupervised learning based moire removal. The recaptured images are corrected against vignetting artifacts to make MR-GAN focus on correcting brightness and contrast distortions while removing moire patterns. We propose two complementary discriminator groups to effectively distinguish moire patterns and image features at both large scales and small scales. A group of self-supervised loss functions, including cycle consistent loss, identity loss, cosine similarity loss, and content leakage loss, are designed to train effective generators. Experimental results demonstrate that our method outperforms state-of-the-art demoireing methods on a large set of test images. Our code and dataset are released in the link: https://github.com/JerryLeolfl/pytorch-MRGAN-master .

Las imágenes infrarrojas presentan defectos asociados al hardware, como son: píxeles defectuosos, ruido temporal, ruido espacial, vignetting entre otros; e inherentes al medio como cambios de iluminación, corrientes de convección y conducción. En este trabajo se hace uso de metodologías enfocadas en la corrección de no uniformidades como lo es el algoritmo de las estadísticas constantes (CS) y rango constante (CR), así mismo se utiliza la morfología matemática y mapeos de contraste (MC) con el objetivo de maximizar el contraste y compensar los cambios de iluminación de cada uno de los frames infrarrojos. Las métricas utilizadas para evaluar el desempeño de las técnicas corresponden al RMSE (error cuadrático medio) y al índice de merito Rho el cual es un estimativo de la cantidad de ruido eliminado, los resultados obtenidos con la utilización de las técnicas de corrección de no uniformidades presentan un índice de merito Rho y un RMSE bajo en comparación a los valores de estas métricas para frames contaminados, siendo estos cercanos a valores de las métricas obtenidos para un frame sin ruido espacial, aun, mejores resultados se obtienen cuando se aplican los operadores morfológicos y mapeos de contraste a las secuencias de imágenes provenientes de la etapa de corrección de ruido, dado que el contraste de la imagen es mejorado adaptativamente.

Background Fundus photography is an imaging modality exclusively used in ophthalmology for visualizing structures like macula, retina, and optic disc. The fundus camera has only one illumination source, which is situated at its center. Hence, structures away from the center will appear darker than naturally they are. This adverse effect caused by the uneven illumination is called as ‘vignetting’. Objectives An algorithm termed as Gamma Correction of Illumination Component (GCIC) for devignetting fundus images is proposed in this paper. Methods Inspired by the Retinex theory, the illumination component is computed with the help of a Maximum a Posteriori (MAP) estimator. The estimated illumination component after normalization is subjected to the Gamma correction to suppress its unevenness. Results GCIC exhibited comparatively low values of Average Gradient of the Illumination Component (AGIC), Lightness Order Error (LOE), and computational time. The proposed method gave a comparatively better performance in terms of the performance metrics, namely contrast-to-noise-ratio (CNR), peak-signal-to-noise-ratio (PSNR), structure similarity index (SSIM), and entropy. With respect to the cumulative performance, GCIC has been observed to be better than other devignetting algorithms in the literature, like Illumination Equalization model, Homomorphic Filtering, Adaptive Gamma Correction (AGC), Modified Sigmoid Transform (MST), Imran Qureshi et al. (2019), Zheng et al., Variation-based Fusion (VF) and Zhou’s et al. Conclusion GCIC corrects the uneven background illumination without scaling or boosting it intolerably. It produces output images, which are natural in appearance, free from color artefacts, and maintaining the sharpness of the fundus features. It is computationally fast as well.

The paper “Model-based local thresholding for canopy hemispherical photography” describes a method that is a framework in which different algorithms could be developed. The basic software needed for algorithm developing was programmed in the R package CAnopy IMage ANalysis (https://github.com/GastonMauroDiaz/caiman). The algorithm described in the paper is a R script published as a gist (https://goo.gl/zygWDN). This project contains data needed to run the script. Also, includes the templates needed to calibrate the camera.

Existing optical see-through (OST) head-mounted displays (HMDs) generally utilize plane mirrors with partial transmission to merge the virtual and real-world scenes, which inducing the vignetting effect to result in small field of view (FOV). Accordingly, this paper proposes an OST-HMD system in which a rotationally-symmetric eyepiece with a wide FOV of ±30 degrees is coupled with a dihedral corner reflector array (DCRA) to achieve a sufficient relief distance to project all of the exit rays of the eyepiece into the pupil. The feasibility of the proposed system is evaluated by means of numerical simulations based on optical models of the HMD lens, DCRA and human eye. The simulation results show that the eye can receive the display information in the wide FOV with a sufficient relief distance through this kind of the OST-HMD development.

Chemiluminescence (CL) is a dominant technology in clinical diagnosis. In order to meet the increasing demand for the sensitive and simultaneous detection of chemiluminescence from multiple samples, the development of multiplex analysis on a single chip is highly desired. However, most chemiluminescence detection systems for multiple samples are still simple lens-based optical imaging systems, and a compromise must always be made between a large aperture (required by the weak chemiluminescence) and a large field of view (required by the size of the sample array). In this paper, we report a fiber bundle-based chemiluminescence detection system for the simultaneous and efficient detection of multiple chemiluminescent samples. In this system, one side of the fiber bundle is directly coupled to the optically active surface of a charge-coupled device (CCD), while the other end is divided into many sub-bundles that are aligned above the samples in a chemiluminescence array to collect their chemiluminescence. Taking advantage of the large numerical aperture and high transmittance of optical fibers, this system shows about a 50 times increase in chemiluminescence collection efficiency over the lens-based imaging system. Moreover, it shows no vignetting effect that is inevitable in a lens-based imaging system with a large field of view. This work provides a promising method for multiple sample chemiluminescence detection, and should find application in bioassays.

Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st.

A vignetting profile form is incorporated with characteristic X‐ray emission data from a Johann‐mounted crystal diffractometer. We prove the validity of the specific form of the vignetting profile. A new characterisation of the Kα profile for vanadium is presented, which supports and is superior to the current benchmark. Using the profile form as a correction for systematic vignetting reduces energy uncertainties by up to a factor of two or more. The greater precision in measurement robustness allows current atomic theories and profiles to be tested with higher levels of accuracy.

Conventional wide-field-of-view cameras consist of multi-lens optics and flat image sensor arrays, which makes them bulky and heavy. As a result, they are poorly suited to advanced mobile applications such as drones and autonomous vehicles. In nature, the eyes of aquatic animals consist of a single spherical lens and a highly sensitive hemispherical retina, an approach that could be beneficial in the development of synthetic wide-field-of-view imaging systems. Here, we report an aquatic-vision-inspired camera that consists of a single monocentric lens and a hemispherical silicon nanorod photodiode array. The imaging system features a wide field of view, miniaturized design, low optical aberration, deep depth of field and simple visual accommodation. Furthermore, under vignetting, the photodiode array enables high-quality panoramic imaging due to the enhanced photodetection properties of the silicon nanorod photodiodes. By integrating a single monocentric lens with a hemispherical silicon nanorod photodiode array, a wide-field-of-view camera is created that offers low optical aberration, deep depth of field and simple visual accommodation.

This paper presents a general plenoptic image formation model based on light ray radiometry. Most existing plenoptic image formation models focus on projection geometry inside a camera equipped with a specific optical device, such as an array of micro-thin-lenses, while light ray radiometry is incomplete or simply neglected. This study aims to describe a general view of light ray radiometry that is then used to define a plenoptic image formation model. This model is based on a radiometry model and light ray projection geometry; hence, it explains phenomena, such as light transport and light direction. Moreover, the proposed plenoptic image formation model describes several radiometric phenomena, such as haze, defocus blur or natural vignetting. The genericity of this model is shown by retrieving existing non-plenoptic models from it. Moreover, the proposed model is assessed by simulating various phenomena, including vignetting and defocus blur, from both synthetic and real data.

French cinema theaters follow strict rules; there are two standards that must be followed when granting theater operation licenses. One standard is concerned with architectural considerations and the other movie reproduction parameters. These two standards undergo periodic revisions. The main goal behind these specifications is to ensure that movies are reproduced uniformly in all theaters while matching the intention of the creative team during the final check. Another goal is audience satisfaction and the avoidance of unnecessary physical or audio visual stress. The specifications listed in the standard related to movie reproduction include a uniformity formula. Counterexamples demonstrate problems with the validity of this uniformity. A new uniformity method of measurement is under study. The proposed method unifies projection and display systems in the same measurement. The projection vignetting effect is canceled using the current room and screen dimensions. The formula is also independent of the reproduction system’s dynamic range. Nine locations on screen are used as points of measurement. The proposed measurement of uniformity is based on a statistical measurement called the Kolmogorov–Smirnov (K–S) distance. It compares the current results with a standard statistical distribution. The type of distribution is chosen based on practical considerations.

A sensor instrumentation and an automated process are proposed for sea-ice field analysis using ship mounted machine vision cameras with the help of inertial and satellite positioning sensors. The proposed process enables automated acquisition of sea-ice concentration, floes size and distribution. The process contains pre-processing steps such as sensor calibration, distortion removal, orthorectification of image data, and data extraction steps such as sea-ice floe clustering, detection, and analysis. In addition, we improve the state of the art of floe clustering and detection, by using an enhanced version of the k-means algorithm and the blue colour channel for increased contrast in ice detection. Comparing to manual visual observations, the proposed method gives significantly more detailed and frequent data about the size and distribution of individual floes. Through our initial experiments in pack ice conditions, the proposed system has proved to be able to segment most of the individual floes and estimate their size and area.

We quantitatively investigate multiple algorithms for microlens array grid estimation for microlens array-based light field cameras. Explicitly taking into account natural and mechanical vignetting effects, we propose a new method for microlens array grid estimation that outperforms the ones previously discussed in the literature. To quantify the performance of the algorithms, we propose an evaluation pipeline utilizing application-specific ray-traced white images with known microlens positions. Using a large dataset of synthesized white images, we thoroughly compare the performance of the different estimation algorithms. As an example, we apply our results to the decoding and calibration of light fields taken with a Lytro Illum camera. We observe that decoding as well as calibration benefit from a more accurate, vignetting-aware grid estimation, especially in peripheral subapertures of the light field., \copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Seamless image stitching depends not only on the accurate alignments of camera images, but also on the compensation of illumination inconsistencies. Even if two images are aligned perfectly, the seam is still visible if the images have a distinct vignetting or different exposure. Image stitching is used to expand the field of view, but a visible seam can lead to significant errors in subsequent visual perception tasks. As a result, we present a straightforward and accurate method for vignetting and exposure correction for stitched images. Firstly, we estimate the camera response function that maps irradiance to intensity. Then, the vignetting model is determined, which is applied to the irradiance images. After that, the exposure of the stitched images is corrected with the irradiance values at the seam. Finally, the irradiance is converted back into intensity using the camera response function. Our approach is evaluated using data recorded by our experimental vehicle and the public nuScenes dataset. Thereby, we test the performance of our method using the IoU of the histograms as well as the mean absolute error of the intensity values in the overlapping image regions. Further more, we demonstrate the real-time capability of our approach.

In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera’s sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Lukás et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.

In standard photography, vignetting is considered mainly as a radiometric effect because it results in a darkening of the edges of the captured image. In this paper, we demonstrate that for light field cameras, vignetting is more than just a radiometric effect. It modifies the properties of the acquired light field and renders most of the calibration procedures from the literature inadequate. We address the problem by describing a model and camera-agnostic method to evaluate vignetting in phase space. This enables the synthesis of vignetted pixel values, which applied to a range of pixels yield images corresponding to the white images that are customarily recorded for calibrating light field cameras. We show that the commonly assumed reference points for microlens-based systems are incorrect approximations to the true optical reference, i.e., the image of the center of the exit pupil. We introduce a novel calibration procedure to determine this optically correct reference point from experimental white images. We describe the changes vignetting imposes on the light field sampling patterns and, therefore, the optical properties of the corresponding virtual cameras using the equivalent camera array model [L. Mignard-Debise, J. Restrepo, and I. Ihrke, “A unifying first-order model for light-field cameras: The equivalent camera array,” IEEE Trans. Comput. Imag. , vol. 3, no. 4, pp. 798–810, Dec. 2017] and apply these insights to a custom-built light field microscope.

The lens design problem for the 2021 IODC is to design a 100 mm focal length lens where if the lens is flipped end-to-end but the radii are not reversed in sign, the lens has to perform the same as in its original form. The lens is used monochromatically. The goal of the problem is to maximize the product of the entrance pupil diameter and the semi-field of view while holding the RMS wavefront error to ≤ 0.070 wave within the field of view. There were 43 entries from 13 different countries. Three different commercial lens design programs were used, along with four in-house programs. The winning entry from Damien Gawron has an entrance pupil diameter of 198.4083 mm and a semi-field of view of 90° for a merit function product of 17,857 out of a maximum possible 18,000.

The filter-ratio technique to remotely measure electron temperature and speed using four color filters in visible light and a polarization camera was described in detail in previous works, in which we quantified the systematic error associated with using models of a symmetric corona to interpret results from an asymmetric corona. We also showed the criteria applied to select the bandwidths of filters and the pros and cons of replacing the traditional linear polarizer with a polarization camera to measure pB. What started in the 1990s by ground experiments conducted during total solar eclipses that lasted for a few minutes led to a balloon-borne experiment lasting eight hours in 2019 and will blossom into a space experiment on the International Space Station in 2023. Due to constraints on the bandwidths of the four filters, a successful mission requires quantification of the statistical error using Monte Carlo simulations to generate two feasibility profiles, which are unique to the design parameters of the coronagraph, to comprehend the feasibility to measure temperature and speed within the desired temporal and spatial resolutions. For the statistical error analysis, we use modeled K- and F-corona profiles, representative theoretical diffraction, scattering, and vignetting profiles, assumed efficiencies for lenses, mirrors, and polarizers, assumed detector properties on quantum efficiency, full-well depth, dark noise, and read noise, and assumed instrument properties on aperture diameter, solid angle, and pixel resolution.

Direct visual odometry algorithms assume that every frame from the camera has the same photometric characteristics. However, the cameras with auto exposure are widely used outdoors as the environment often changes. The vignetting also affects the pixel’s brightness on different frames, even if the exposure time is fixed. We propose an online vignetting correction and exposure time estimation method for stereo direct visual odometry algorithms. Our method works on a camera that has a gamma-like response function. The inverse vignetting function and exposure time ratio between neighboring frames are estimated. Stereo matching is used to select correspondences between the left image and right image in the same frame at the initialization step. Feature points are used to pick the correspondences between different frames. Our method provides static correction results during the experiments on datasets and a stereo camera.

The camera is affected by illumination changes and vignetting effect during the shooting process, resulting in the lack of brightness information, which affects the accuracy of target detection. In order to reduce the impact of illumination changes and vignetting effect on image quality, obtain images closer to the real scene, and improve the accuracy of target detection, this paper conducts photometric calibration on the image from two aspects: the calibration of CRF (Camera Response Function) and the compensation function of vignetting effect. This paper first uses the N-degree polynomial model to construct the camera response function, proposes a functional model of vignetting compensation based on the position of the pixel point, and finally combines the first two experimental results and conducts experimental verification on the data set before and after the photometric calibration on different algorithms of target detection. The experimental results show that the application of photometric calibration in the field of machine vision can improve the adaptability of machine vision to illumination changes and vignetting effect to a certain extent. The self-made data set is trained and tested, when IoU is 0.7, the mAP calculated by the test set after photometric calibration is improved by 2.3% compared with the original data set.