Gray world algorithm typically shows poor illuminant estimation performance due to scene variations of natural images. To address the above problem, an improved gray world algorithm guided by scene semantics is proposed. Firstly, dense-SIFT descriptors are calculated using grayscale images to avoid the interference of color-biased images, followed by the bag of words (BoW) model to generate unordered visual vocabulary. Secondly, based on the average brightness of these visual words, word frequency histograms are constructed weighted by brightness using the spatial pyramid matching (SPM) algorithm. Thirdly, the scene semantics similarity between images employing the histogram cross kernel function, and retrieve a set of candidate images that similar to the test image is calculated. Finally, after removing the outlier images using the isolated forest algorithm, the pixel statistics distributions of all remaining images are used to adaptively infer and update the fixed assumption of the gray world algorithm. The improved gray world algorithm implements color constancy through illuminant estimation. Experimental results in three publicly available color constancy datasets (ColorChecker, Cube+ and NUS) show that the proposed algorithm outperforms comparable improved gray world algorithms in single camera tests (nearly 20% angular error improvement), at the same time, achieves optimal performance in cross-camera tests.

Progressive Multi-Scale Feature Cascade Fusion Color Constancy Algorithm

The phenomenon of color constancy has often been appealed to in philosophical discussions of the nature and perception of colors. In these discussions, two ways of interpreting the role of illumination and illuminants in color vision are prominent. Color realists and objectivists argue that colors are illumination-independent properties because they are perceived and recognized despite changes in illumination. Color relationalists and subjectivists, on the other hand, deny that colors remain constant across changes in illumination and conclude that colors are relative and illumination-dependent properties. I offer an alternative to these opposing views and argue that colors are illumination-dependent but also objective and intrinsic properties of surfaces. The result is an entirely original approach to the role of illumination and illuminants in color perception.

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Three challenging image datasets collected by multiple cameras under various illumination and exposure conditions, including one that imitates uncommon scenes such as scientific imaging, were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.

Correction to: Proposal and evaluation for color constancy CAPTCHA

Perception adapts to the properties of prior stimulation, as illustrated by phenomena such as visual color constancy or speech context effects. In the auditory domain, only little is known about adaptive processes when it comes to the attribute of auditory brightness. Here, we report an experiment that tests whether listeners adapt to spectral colorations imposed on naturalistic music and speech excerpts. Our results indicate consistent contrastive adaptation of auditory brightness judgments on a trial-by-trial basis. The pattern of results suggests that these effects tend to grow with an increase in the duration of the adaptor context but level off after around 8 trials of 2 s duration. A simple model of the response criterion yields a correlation of r = .97 with the measured data and corroborates the notion that brightness perception adapts on timescales that fall in the range of auditory short-term memory. Effects turn out to be similar for spectral filtering based on linear spectral filter slopes and filtering based on a measured transfer function from a commercially available hearing device. Overall, our findings demonstrate the adaptivity of auditory brightness perception under realistic acoustical conditions.

It is well known that color formation acts as a noise-reducing lossy compression mechanism that results in ambiguity, known as metamerism. Surfaces that match under one set of conditions-an illuminant and observer or capture device-can mismatch under others. The phenomenon has been studied extensively in the past, leading to important results like metamer mismatch volumes, color correction, reflectance estimation and the computation of metamer sets-sets of all possible reflectances that could result in a given sensor response. However, most of these approaches have three limitations: first, they simplify the problem and make assumptions about what reflectances can look like (i.e., being smooth, natural, residing in a subspace based on some measured data), second, they deal with strict mathematical metamerism and overlook noise or precision, and third, only isolated responses are considered without taking the context of a response into account. In this paper we address these limitations by outlining an approach that allows for the robust computation of approximate unconstrained metamer sets and exact unconstrained paramer sets. The notion of spatial or relational paramer sets that take neighboring responses into account, and applications to illuminant estimation and color constancy are also briefly discussed.

Virtual reality (VR) technology offers vision researchers the opportunity to conduct immersive studies in simulated real-world scenes. However, an accurate colour calibration of the VR head mounted display (HMD), both in terms of luminance and chromaticity, is required to precisely control the presented stimuli. Such a calibration presents significant new challenges, for example, due to the large field of view of the HMD, or the software implementation used for scene rendering, which might alter the colour appearance of objects. Here, we propose a framework for calibrating an HMD using an imaging colorimeter, the I29 (Radiant Vision Systems, Redmond, WA, USA). We examine two scenarios, both with and without using a rendering software for visualisation. In addition, we present a colour constancy experiment design for VR through a gaming engine software, Unreal Engine 4. The colours of the objects of study are chosen according to the previously defined calibration. Results show a high-colour constancy performance among participants, in agreement with recent studies performed on real-world scenarios. Our studies show that our methodology allows us to control and measure the colours presented in the HMD, effectively enabling the use of VR technology for colour vision research.

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

At present, the human visual perception system is the most effective, accurate, and fast image processing system in the world. This is because human eyes have some special visual features, but the features closely related to image enhancement include color constancy and brightness constancy. This paper presents a new image enhancement framework and computational model which can better simulate human visual features. It is based on the analysis of color constancy and luminance constancy and Retinex theory. And, this is a new image enhancement method in the compressed domain based on Retinex theory. In Retinex theory, DCT coefficients consist of incident components (DC coefficients) and reflection components (AC coefficients). By adjusting the dynamic range of DC coefficients, carefully adjusting AC coefficients, and using the threshold method for block suppression, the compressed domain image can be enhanced. On the basis of Retinex theory, the incident light and reflected light components are considered synthetically, the dynamic range (DC coefficient) of the incident light component and the details of the reflected light component (AC coefficient) are adjusted, and then the incident light component is reexamined. Moreover, it achieves a better image enhancement effect and avoids the blocking effect.

Abstract Many kinds of research have been undertaken on color constancy under various illuminations; however, not many people have studied this domain in the context of vivid colored light. In this article, 23 color chips were assessed using the elementary color‐naming method under 13 RGB‐LED illuminations of six hues as red, yellow, green, cyan, blue, and magenta. A total of 100 subjects participated in the study and reported that color chips of cyan, yellow, and green light were not good as compared to the others. This article also proposes an elementary color‐naming method as an alternative way to study and calculate color constancy index using the two rooms technique as extending experiment booth to get the color appearance of the color chip without adapting to the colored illumination. Our results suggested individual color appearance performance for each colored light which yellow, cyan, and green are not recommended as shown by poor area ratio and color constancy index.

This paper presents a three-color balance adjustment for color constancy correction. White balancing is a typical adjustment for color constancy in an image, but there are still lighting effects on colors other than white. Cheng et al. proposed multi-color balancing to improve the performance of white balancing by mapping multiple target colors into corresponding ground truth colors. However, there are still three problems that have not been discussed: choosing the number of target colors, selecting target colors, and minimizing error which causes computational complexity to increase. In this paper, we first discuss the number of target colors for multi-color balancing. From our observation, when the number of target colors is greater than or equal to three, the best performance of multi-color balancing in each number of target colors is almost the same regardless of the number of target colors, and it is superior to that of white balancing. Moreover, if the number of target colors is three, multi-color balancing can be performed without any error minimization. Accordingly, we propose three-color balancing. In addition, the combination of three target colors is discussed to achieve color constancy correction. In an experiment, the proposed method not only outperforms white balancing but also has almost the same performance as Cheng’s method with 24 target colors.

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a ‘white’ illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.

In this article we show the change in paradigm occurred in color constancy algorithms: from a pre-processing step in image understanding, to the exploitation of image understanding and computer vision results and techniques. Since color constancy is an ill-posed problem, we give an overview of the assumptions on which classical color constancy algorithms are based in order to solve it. Then, we chronologically review the color constancy algorithms that exploit results and techniques borrowed from the image understanding research field in order to exploit assumptions that could be met in a larger number of images.

The London Imaging Meeting is a yearly topics-based conference organized by the Society of Imaging Science and technology (IS&T), in collaboration with the Institute of Physics (IOP) and the Royal Photographic Society. This year's topic was "Imaging for Deep Learning". At the heart of our conference were five focal talks given by worldrenowned experts in the field (who then also organised the related sessions). Focal speakers were Dr. Seyed Ali Amirshahi, NTNU, Norway (Image Quality); Prof. Jonas Unger, Linköping University, Sweden (Datasets for Deep Learning); Prof. Simone Bianco, Università degli Studi di Milano-Bicocca, Italy (Color Constancy); Dr. Valentina Donzella, University of Warwick, UK (Imaging Performance); and Dr. Ray Ptucha, Apple Inc. US (Characterization and Optimization). We also had two superb keynote speakers. Thanks to Dr. Robin Jenkin, Nvidia, for his talk on "Camera Metrics for Autonomous Vision" and to Dr. Joyce Farrell, Stanford University, for her talk on "Soft Prototyping Camera Designs for Autonomous Driving". As a new innovation this year—and to support the remit of LIM to reach out to students in the field—we included an invited tutorial research lecture. Given by Prof. Stephen Westland, University of Leeds, the presentation titled "Using Imaging Data for Efficient Colour Design" looked at deep learning techniques in the field of design and demonstrated that simple applications of deep learning can deliver excellent results. There were many strong contenders for the LIM Best Paper Award. Noteworthy, honourable mentions include "Portrait Quality Assessment using Multi-scale CNN", N. Chahine and S. Belkarfa, DXOMARK' "HDR4CV: High dynamic range dataset with adversarial illumination for testing computer vision methods", P. Hanjil et al., University of Cambridge; "Natural Scene Derived Camera Edge Spatial Frequency Response for Autonomous Vision Systems", O. van Zwanenberg et al., University of Westminster; and "Towards a Generic Neural Network Architecture for Approximating Tone Mapping Algorithms", J. McVey and G. Finlayson, University of East Anglia. But, by a unanimous vote, this year's Best Paper was awarded to "Impact of the Windshield's Optical Aberrations on Visual Range Camera-based Classification Tasks Performed by CNNs", C. Krebs, P. Müller, and A. Braun, (Hochschule Düsseldorf) (University of Applied Sciences Düsseldorf), Germany. We thank everyone who helped make LIM a success including the IS&T office, and the LIM presenters, reviewers, focal speakers, and keynotes, as well as the audience, who participated in making the event engaging and vibrant. This year, the conference was run by the IOP and we are extremely grateful for their help in hosting the event. A final special thanks go to the Engineering and Physical Sciences Research Council (EPSRC) who provided funding through the grant EP/S028730/1. Finally, we are pleased to announce that next year's LIM conference will be in the area of "Displays"; the conference chair is Dr. Rafal Mantiuk, University of Cambridge. —Prof. Graham Finlayson, LIM series chair, and Prof. Sophie Triantaphillidou, LIM2021 conference chair

Computational color constancy algorithms are commonly evaluated only through angular error analysis on annotated datasets of static images. The widespread use of videos in consumer devices motivated us to define a richer methodology for color constancy evaluation. To this extent, temporal and spatial stability are defined here to determine the degree of sensitivity of color constancy algorithms to variations in the scene that do not depend on the illuminant source, such as moving subjects or a moving camera. Our evaluation methodology is applied to compare several color constancy algorithms on stable sequences belonging to the Gray Ball and Burst Color Constancy video datasets. The stable sequences, identified using a general-purpose procedure, are made available for public download to encourage future research. Our investigation proves the importance of evaluating color constancy algorithms according to multiple metrics, instead of angular error alone. For example, the popular fully convolutional color constancy with confidence-weighted pooling algorithm is consistently the best performing solution for error evaluation, but it is often surpassed in terms of stability by the traditional gray edge algorithm, and by the more recent sensor-independent illumination estimation algorithm.

In order to solve the image blurring and distortion problem caused by underwater non-uniform and low illumination, this paper proposes an underwater image enhancement algorithm based on the Retinex theory and the Alternating Direction Method of Multipliers (ADMM). Firstly, the L component of the original image in the Lab space is extracted as the initial illumination map, and an Augmented Lagrange Multiplier (ALM) framework is constructed based on the ADMM to optimize the initial illumination map in order to obtain an accurate illumination image. In addition, the illumination map is further corrected in the luminance region with the Gamma Correction. Secondly, combined with the color constancy characteristics in the Retinex theory, the reflected image of the object is obtained. Finally, the bilateral filter is picked to suppress the underwater noise and obtain a more detailed enhanced image. The experimental results show that the underwater image enhancement algorithm can effectively solve the non-uniform illumination problem caused by natural light or artificial light source and improve the underwater image quality, thus having a better performance than other algorithms.

Underwater image enhancement by combining color constancy and dehazing based on depth estimation

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

Colour category constancy and the development of colour naming