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22 pags., 13 figs., 3 tabs. -- This work is dedicated to the memory of Jihyun Yeonan-Kim., In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images., Funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 761544 (project HDR4EU) and under grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant ref. PGC2018-099651-BI00 (MCIU/AEI/FEDER, UE). JVC received funding from the project PID2019-109628RJI00/AEI/10.13039/501100011033 by the Ministerio de Ciencia, Innovación y Universidades (MCIU) and the Agencia Estatal de Investigación (AEI) of the Spanish government.

In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including $\Delta I T P_{R}$ , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components .

The responses of visual neurons, as well as visual perception phenomena in general, are highly nonlinear functions of the visual input, while most vision models are grounded on the notion of a linear receptive field (RF). The linear RF has a number of inherent problems: it changes with the input, it presupposes a set of basis functions for the visual system, and it conflicts with recent studies on dendritic computations. Here we propose to model the RF in a nonlinear manner, introducing the intrinsically nonlinear receptive field (INRF). Apart from being more physiologically plausible and embodying the efficient representation principle, the INRF has a key property of wide-ranging implications: for several vision science phenomena where a linear RF must vary with the input in order to predict responses, the INRF can remain constant under different stimuli. We also prove that Artificial Neural Networks with INRF modules instead of linear filters have a remarkably improved performance and better emulate basic human perception. Our results suggest a change of paradigm for vision science as well as for artificial intelligence.

Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods. Horizon 2020 Framework Programme (761544, 780470); Engineering and Physical Sciences Research Council (EP/028730, EP/M001768); Spanish Government MINECO and Feder Fund (PGC2018-099651-B-I00).

We present a color matching method that, given two different views of the same scene taken by two cameras with unknown settings and unknown internal parameter values, and encoded with unknown non-linear curves, is able to correct the colors of one of the images making it look as if it was captured under the other camera’s settings. Our method is based on treating the in-camera color processing pipeline as a matrix multiplication followed by a non-linearity. This allows us to model a color stabilization transformation among the two shots by estimating several parameters. The method is fast and the results have no spurious colors. It outperforms the state-of-the-art both visually and according to several metrics, and can handle very challenging real-life examples. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 761544 (project HDR4EU) and under grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant ref. TIN2015-71537-P (MINECO/FEDER,UE).

By capturing a more complete rendition of scene light than standard 2D cameras, light-field technology represents an important step towards closing the gap between live action cinematography and computer graphics. Light-field cameras accomplish this by simultaneously capturing the same scene under different angular configurations, providing directional information that allows for a multitude of post-production effects. Among the practical challenges related to capturing multiple images simultaneously, a very important problem is the fact that the different images do not perfectly match in terms of color, which severely complicates all further processing. In this work we adapt and extend to the light-field scenario a color stabilization method previously proposed for standard multi-camera shoots, and demonstrate experimentally that it provides an improvement over the state-of-the-art techniques for light-field imaging. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 761544 (project HDR4EU) and under grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant ref. PGC2018-099651-B-I00 (MCIU/AEI/FEDER, UE).

The study of visual illusions has proven to be a very useful approach in vision science. In this work we start by showing that, while convolutional neural networks (CNNs) trained for low-level visual tasks in natural images may be deceived by brightness and color illusions, some network illusions can be inconsistent with the perception of humans. Next, we analyze where these similarities and differences may come from. On one hand, the proposed linear eigenanalysis explains the overall similarities: in simple CNNs trained for tasks like denoising or deblurring, the linear version of the network has center-surround receptive fields, and global transfer functions are very similar to the human achromatic and chromatic contrast sensitivity functions in human-like opponent color spaces. These similarities are consistent with the long-standing hypothesis that considers low-level visual illusions as a by-product of the optimization to natural environments. Specifically, here human-like features emerge from error minimization. On the other hand, the observed differences must be due to the behavior of the human visual system not explained by the linear approximation. However, our study also shows that more ‘flexible’ network architectures, with more layers and a higher degree of nonlinearity, may actually have a worse capability of reproducing visual illusions. This implies, in line with other works in the vision science literature, a word of caution on using CNNs to study human vision: on top of the intrinsic limitations of the L+NL formulation of artificial networks to model vision, the nonlinear behavior of flexible architectures may easily be markedly different from that of the visual system. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 761544 (project HDR4EU) and under grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant Ref. PGC2018-099651-B-I00 (MCIU/AEI/FEDER, UE). The work of AM was supported by the Spanish government under Grant FJCI-2017–31758. JM has been supported by the Spanish government under the MINECO grant Ref. DPI2017-89867 and by the Generalitat Velanciana grant Ref. GrisoliaP-2019-035. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

We propose a method for color dehazing with four main characteristics: it does not introduce color artifacts, it does not depend on inverting any physical equation, it is based on models of visual perception, and it is fast, potentially real time. Our method converts the original input image to the HSV color space and works in the saturation and value domains by: (1) reducing the value component via a global constrained histogram flattening; (2) modifying the saturation component in consistency with the previous reduced value; and (3) performing a local contrast enhancement in the value component. Results show that our method competes with the state-of-the-art when dealing with standard hazy images, and outperforms it when dealing with challenging haze cases. Furthermore, our method is able to dehaze a FullHD image on a GPU in 90 ms.

14 pags., 14 figs., Gamut mapping is the problem of transforming the colors of image or video content so as to fully exploit the color palette of the display device where the content will be shown, while preserving the artistic intent of the original content’s creator. In particular, in the cinema industry, the rapid advancement in display technologies has created a pressing need to develop automatic and fast gamut mapping algorithms. In this article, we propose a novel framework that is based on vision science models, performs both gamut reduction and gamut extension, is of low computational complexity, produces results that are free from artifacts and outperforms state-of-the-art methods according to psychophysical tests. Our experiments also highlight the limitations of existing objective metrics for the gamut mapping problem., This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant agreement number 761544 (project HDR4EU) and under Grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant ref. PGC2018-099651-B-I00 (MCIU/AEI/FEDER, UE). The work of J. Vazquez-Corral was supported by the Spanish government under Grant IJCI-2014-19516.

Visual illusions teach us that what we see is not always what is represented in the physical world. Their special nature make them a fascinating tool to test and validate any new vision model proposed. In general, current vision models are based on the concatenation of linear and non-linear operations. The similarity of this structure with the operations present in Convolutional Neural Networks (CNNs) has motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions. In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size. These results suggest that in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions.

Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods.

The Chromagenic color constancy algorithm estimates the light color given two images of the same scene, one filtered and one unfiltered. The key insight underpinning the chromagenic method is that the filtered and unfiltered images are linearly related and that this linear relationship correlates strongly with the illuminant color. In the original method the best linear relationship was found based on the assumption that the filtered and unfiltered images were registered. Generally, this is not the case and implies an expensive image registration step.

Multiple-exposure approaches for high dynamic range (HDR) image generation share a set of building assumptions: that color channels are independent and that the camera response function (CRF) remains constant while changing the exposure. The first contribution of this paper is to highlight how these assumptions, which were correct for film photography, do not hold in general for digital cameras. As a consequence, results of multiexposure HDR methods are less accurate, and when tone-mapped they often present problems like hue shifts and color artifacts. The second contribution is to propose a method to stabilize the CRF while coupling all color channels, which can be applied to both static and dynamic scenes, and yield artifact-free results that are more accurate than those obtained with state-of-the-art methods according to several image metrics. This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement number 761544 (project HDR4EU) and under grant agreement number 780470 (project SAUCE), and by the Spanish government and FEDER Fund, grant ref. TIN2015-71537-P (MINECO/FEDER,UE). The work of J. Vazquez-Corral was supported by the Spanish government under Grant IJCI-2014-19516.

Image dehazing tries to solve an undesired loss of visibility in outdoor images due to the presence of fog. Recently, machine-learning techniques have shown great dehazing ability. However, in order to be trained, they require training sets with pairs of foggy images and their clean counterparts, or a depth-map. In this paper, we propose to learn the appearance of fog from weakly-labeled data. Specifically, we only require a single label per-image stating if it contains fog or not. Based on the Multiple-Instance Learning framework, we propose a model that can learn from image-level labels to predict if an image contains haze reasoning at a local level. Fog detection performance of the proposed method compares favorably with two popular techniques, and the attention maps generated by the model demonstrate that it effectively learns to disregard sky regions as indicative of the presence of fog, a common pitfall of current image dehazing techniques.

This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem., 13 pages, 5 figures

Gamut mapping transforms the color gamut of an image to that of a target device. Two cases are usually considered: gamut reduction (target gamut smaller than source gamut), and gamut extension (target gamut larger than the source gamut). Less attention is devoted to the more general case, when neither gamut is fully included in the other. In this work we unify and expand two recent methods for gamut extension and reduction, so as to simultaneously perform both forms of gamut mapping in di erent regions of the same image without introducing color artifacts or halos. We demonstrate the usefulness of this approach for the traditional gamut mapping problem, and also how the proposed method can be used to adapt the color palette of an image so that it is closer to that of a given reference image. Results are compared with the state-of-the-art and validated through user tests and objective metrics. This work is supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government FEDER Fund, grant ref. TIN2015-71537-P(MINECO/FEDER,UE), and by the Icrea Academia Award. The work of J. Vazquez-Corral was supported by the Spanish government under Grant IJCI-2014-19516

The cinema and television industries are continuously working in the development of image features that provide a better visual experience to viewers, increasing spatial resolution, frame rate, contrast, and recently, with emerging display technologies, much more vivid colors. For this reason there is a pressing need to develop fast, automatic and reliable gamut mapping algorithms that can transform the colors of the original content, adapting it to the capabilities of the display or projector system in which it is going to be viewed while at the same time respecting the artistic intent of the creator. In this article we present a review of our work on variational methods for gamut mapping that comply with some basic global and local properties of the human visual system, producing state-of-the-art results that appear natural and are perceptually faithful to the original material.

Blind gamma estimation is the problem of estimating the gamma function that is applied to a linear image both for perceptual reasons and for the compensation of the non-linear behavior of displays. Gamma values change both inter- and intra-camera. In the latter case, the change comes from the use of different scene settings. In this letter we propose a new approach that relies on the use of more than a single image from the same scene. We estimate the gammas for all the different images at the same time with a method based on exploiting the structure of the standard in-camera processing pipeline. Our results improve over the state-of-the-art. This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2012-38112, and by the Icrea Academia Award.

Emerging display technologies are able to produce images with a much wider color gamut than those of conventional distribution gamuts for cinema and TV, creating an opportunity for the development of gamut extension algorithms (GEAs) that exploit the full color potential of these new systems. In this paper, we present a novel GEA, implemented as a PDE-based optimization procedure related to visual perception models, that performs gamut extension (GE) by taking into account the analysis of distortions in hue, chroma, and saturation. User studies performed using a digital cinema projector under cinematic (low ambient light, large screen) conditions show that the proposed algorithm outperforms the state of the art, producing gamut extended images that are perceptually more faithful to the wide-gamut ground truth, as well as free of color artifacts and hue shifts. We also show how currently available image quality metrics, when applied to the GE problem, provide results that do not correlate with users' choices.

The extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to emphasize the fact that the number of channels is adapted to the image content. The higher the color complexity of an image, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding strategy using these color pivots as a basis. To evaluate the proposed approach, we measure the efficiency in an image categorization task. We show how a generic descriptor improves performance at the description level when applied to the MTT coding.

Wide gamut digital display technology, in order to show its full potential in terms of colors, is creating an opportunity to develop gamut extension algorithms (GEAs). To this end, in this work we present two contributions. First we report a psychophysical evaluation of GEAs specifically for cinema using a digital cinema projector under cinematic (low ambient light) conditions; to the best of our knowledge this is the first evaluation of this kind reported in the literature. Second, we propose a new GEA by introducing simple but key modifications to the algorithm of Zamir et al. This new algorithm performs well in terms of skin tones and memory colors, with results that look natural and which are free from artifacts. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. This work was supported by the European Research Council, Starting Grant ref. 306337, by Spanish grant ref. TIN2012-38112 and by ICREA Academia

In this paper we present a novel approach to the problem of exposure fusion of a stack of pictures for the generation of high dynamic range (HDR) radiance maps. All exposure fusion approaches, when applied on 8-bit non-RAW pictures, perform photometric/ncalibration by estimating and inverting the camera response function, which is assumed to be a channelwise-independent function which does not change with the exposure. Our experiments show that these assumptions do not always hold and that the camera may automatically introduce changes (in gain, white balance, gamma correction value) from one exposure to the next when performing the non-linear operations involved in recording pictures in non-RAW formats such as JPEG. The net result is that HDR radiance maps obtained from exposure fusion of non-linear data may have substantially more error than if computed directly from the linear, RAW data. Our proposed method overcomes this problem and compensates for the changes introduced by the camera by matching the color correction and gamma correction transforms of all pictures to those of a reference picture in the stack, providing a clear improvement in terms of PSNR with respect to the classical method of Debevec and Malik. This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2012-38112, and by the Icrea Academia Award

Comunicació presentada a: European Conference on Computer Vision Workshops (ECCV 2014), celebrada del 6 al 7 de setembre de 2014 a Zurich, Suïssa. Images captured under adverse weather conditions, such as haze or fog, typically exhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling the image struc- ture under the haze layer and recovering vivid colors out of a single image remains a challenging task, since the degradation is depth-dependent and conventional methods are unable to handle this problem. We propose to extend a well-known perception-inspired variational frame- work [1] for the task of single image dehazing. The main modification consists on the replacement of the value used by this framework for the grey-world hypothesis by an estimation of the mean of the clean image. This allows us to devise a variational method that requires no estimate of the depth structure of the scene, performing a spatially-variant contrast enhancement that effectively removes haze from far away regions. Exper- imental results show that our method competes well with other state- of-the-art methods in typical benchmark images, while outperforming current image dehazing methods in more challenging scenarios. JVC and MB were supported by European Research Council, Starting Grant ref. 306337, and by Spanish grants ref. TIN2011-15954-E and ref. TIN2012-38112.

Color camera characterization, mapping outputs from the camera sensors to an independent color space, such as (XYZ), is an important step in the camera processing pipeline. Until now, this procedure has been primarily solved by using a (3 times 3) matrix obtained via a least-squares optimization. In this paper, we propose to use the spherical sampling method, recently published by Finlayson al., to perform a perceptual color characterization. In particular, we search for the (3 times 3) matrix that minimizes three different perceptual errors, one pixel based and two spatially based. For the pixel-based case, we minimize the CIE (Delta E) error, while for the spatial-based case, we minimize both the S-CIELAB error and the CID error measure. Our results demonstrate an improvement of approximately 3for the (Delta E) error, 7&amp, for the S-CIELAB error and 13% for the CID error measures.

It has now been 20 years since the seminal work by Finlayson et al. on the use/nof spectral sharpening of sensors to achieve diagonal color constancy. Spectral sharpening is/nstill used today by numerous researchers for different goals unrelated to the original goal/nof diagonal color constancy e.g., multispectral processing, shadow removal, location of/nunique hues. This paper reviews the idea of spectral sharpening through the lens of what/nis known today in color constancy, describes the different methods used for obtaining a set/nof sharpening sensors and presents an overview of the many different uses that have been/nfound for spectral sharpening over the years. This work was supported by European Research Council, Starting Grant ref. 306337, and by Spanish/ngrants ref. TIN2011-15954-E, and ref. TIN2012-38112

Comunicació presentada a: 6th Pacific-Rim Symposium on Image and Video Technology, (PSIVT 2013), celebrada del 28 d'octubre a l'1 de novembre a Guanajuato, Mèxic. In this paper we present a spatial gamut mapping algorithm that relies on a perceptually-based variational framework. Our method adapts a well-known image energy functional whose minimization leads to image enhancement and contrast modification. We show how by varying the importance of the contrast term in the image functional we are able to perform gamut reduction. We propose an iterative scheme that allows our algorithm to successfully map the colors from the gamut of the original image to a given destination gamut while preserving the colors’ perception and texture close to the original image. Both subjective and objective evaluation validate the promising results achieved via our proposed framework. This work was supported by the European Research Council, Starting Grant ref. 306337, and by Spanish grants ref. TIN2011-15954-E and ref. TIN2012-38112.

Gamut mapping transforms the colors of an input/nimage to the colors of a target device so as to exploit the full/npotential of the rendering device in terms of color rendition. In/nthis paper we present spatial gamut mapping algorithms that rely/non a perceptually-based variational framework. Our algorithms/nadapt a well-known image energy functional whose minimization/nleads to image enhancement and contrast modification. We show/nhow by varying the importance of the contrast term in the/nimage functional we are able to perform gamut reduction and/ngamut extension. We propose an iterative scheme that allows our/nalgorithms to successfully map the colors from the gamut of the/noriginal image to a given destination gamut while keeping the/nperceived colors close to the original image. Both subjective and/nobjective evaluations validate the promising results achieved via/nour proposed algorithms. This work was supported by the European Research Council,/nStarting Grant ref. 306337, and by Spanish grants/nref. TIN2011-15954-E and ref. TIN2012-38112.

A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves.

When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data.

The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evalu- ation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical il- luminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate il- luminants; in this case selection is based on increasing the perfor- mance on the subsequent steps of the systems. In this paper the authors propose a new performance measure, the perceptual angu- lar error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experi- ment comparing solutions from three different color constancy algo- rithms. Our results show that in more than half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral gray sphere, which better copes with the illuminant variations of the scene. © 2009 Society for Imaging Science and Technology. DOI: XXXX

Images obtained under adverse weather conditions, such as haze or fog, typically/nexhibit low contrast and faded colors, which may severely limit the visibility within the scene. Unveiling/nthe image structure under the haze layer and recovering vivid colors out of a single image/nremains a challenging task, since the degradation is depth-dependent and conventional methods are/nunable to overcome this problem. In this work, we extend a well-known perception-inspired variational/nframework for single image dehazing. Two main improvements are proposed. First, we replace/nthe value used by the framework for the grey-world hypothesis by an estimation of the mean of/nthe clean image. Second, we add a set of new terms to the energy functional for maximizing the/ninter-channel contrast. Experimental results show that the proposed Enhanced Variational Image/nDehazing (EVID) method outperforms other state-of-the-art methods both qualitatively and quantitatively./nIn particular, when the illuminant is uneven, our EVID method is the only one that recovers/nrealistic colors, avoiding the appearance of strong chromatic artifacts. D. Pardo was partially funded by the Project of the Spanish Ministry of Economy and Competitiveness with reference MTM2013-40824-P, the BCAM “Severo Ochoa” accreditation of excellence SEV-2013-0323, the CYTED 2011 project 712RT0449, and the Basque Government/nConsolidated Research Group Grant IT649-13 on “Mathematical Modeling, Simulation, and Industrial Applications (M2SI)”.

Comunicació presentada al IS&T International Symposium on Electronic Imaging, celebrat del 14 al 18 de febrer de 2016 a San Francisco (CA, USA) i organitzat per la Society for Imaging Science and Technology. The retinex theory of color vision has been a light of inspiration for color researchers since its inception 50 years ago. It has been adapted to work for different goals such as shadow removal, high dynamic range imaging, or computational color constancy. Back in 2007 a variational perceptually-based color correction model related to Retinex was presented by Bertalmío and colleagues. In this paper we first comment on this model and later we review different image processing applications that have been obtained by performing small modifications to it, namely color gamut mapping and image dehazing. J. Vazquez-Corral. S.W. Zamir, and M. Bertalmío were supported by European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2012-38112, and by the Icrea Academia Award. D. Pardo was partially funded by the Project of the Spanish Ministry of Economy and Competitiveness with reference MTM2013-40824-P, the BCAM ”Severo Ochoa” accreditation of excellence SEV-2013-0323, the CYTED 2011 project 712RT0449, and the Basque Government Consolidated Research Group Grant IT649-13 on “Mathematical Modeling, Simulation, and Industrial Applications (M2SI)”.

High dynamic range imaging techniques involve capturing and storing real world radiance values that span many orders of magnitude. However, common display devices can usually reproduce intensity ranges only up to two to three orders of magnitude. Therefore, in order to display a high dynamic range image on a low dynamic range screen, the dynamic range of the image needs to be compressed without losing details or introducing artefacts, and this process is called tone mapping. A good tone mapping operator must be able to produce a low dynamic range image that matches as much as possible the perception of the real world scene. We propose a two stage tone mapping approach, in which the first stage is a global method for range compression based on a gamma curve that equalizes the lightness histogram the best, and the second stage performs local contrast enhancement and color induction using neural activity models for the visual cortex. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2012-38112, and by the Icrea Academia Award.

We propose a method for color stabilization of shots of the same scene, taken under the same illumination, where one image is chosen as reference and one or several other images are modified so that their colors match those of the reference. We make use of two crucial but often overlooked observations: first, that the core of the color correction chain in a digital camera is simply a multiplication by a 3 × 3 matrix; second, that to color-match a source image to a reference image we do not need to compute their two color correction matrices, it is enough to compute the operation that transforms one matrix into the other. This operation is a 3 × 3 matrix as well, which we call H. Once we have H, we just multiply by it each pixel value of the source and obtain an image which matches in color the reference. To compute H we only require a set of pixel correspondences, we do not need any information about the cameras used, neither models nor specifications or parameter values. We propose an implementation of our framework which is very simple and fast, and show how it can be successfully employed in a number of situations, comparing favorably with the state of the art. There is a wide range of applications of our technique, both for amateur and professional photography and video: color matching for multicamera TV broadcasts, color matching for 3D cinema, color stabilization for amateur video, etc. This work was supported in part by the European Research Council under Grant 306337 and in part by the Spanish Research under Grant TIN2011-15954-E and Grant TIN2012-38112.

Comunicació presentada a: IEEE International Conference on Image Processing 2016, celebrada del 25 l 28 de setembre de 2016 a Phoenix, Estats Units d'Amèrica. We propose a method for the color stabilization of cinema shots coming from different cameras that use unknown logarithmic encoding curves. The log-encoding curves are approximated by a concatenation of gamma-curves, whose values are accurately computed using image matches. The color stabilization procedure, based on the generic color processing pipeline of a digital camera, can be performed after the estimation of the encoding curves, and it also requires the existence of image matches. Our work can be applied in different scenarios such as multi-camera shoots, native-3D cinema, or color grading in post-production. This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government, grant ref. TIN2015-71537-P, and by the Icrea Academia Award.

There are many works in colour that assume illumination change can be modelled by multiplying sensor responses by individual scaling factors. The early research in this area are sometimes grouped under the heading von Kries adaptation: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones which have narrower support: they should be applied to the so-called “Sharp Sensors”. In this paper we generalise the computational approach to spectral sharpening in three important ways. First, we introduce Spherical Sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors which minimises a variety of error measures including CIE Delta E (previous work on spectral sharpening minimised RMS) and colour ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of colour signal spectra. Spherical sampling is shown to improve on the state of the art.

Comunicació presentada a: SMPTE 2016 Annual Technical Conference and Exhibition, celebrada a Los Angeles, Estats Units d'Amèrica, del 25 al 27 d'octubre de 2016. While wide color gamut (WCG) capabilities are a key element of emerging display and projection technologies, at present most image content is recorded using standards such as DCI-P3 for cinema or BT.709 for TV that have a reduced color gamut. Therefore, there is a need for gamut extension methods that process regular content and allow to appreciate the full color potential of new displays, improving user experience. We present a gamut extension algorithm that is based on visual perception models and explicitly takes into account low chromatic colors such as skin tones. It produces results that look natural, are free of artifacts of any kind, and outperform the state of the art. The method is fast, allowing for operation interaction if needed. This work was supported by the European Research Council, Starting Grant ref. 306337, by the Spanish government and FEDER Fund, grant ref. TIN2015-71537-P (MINECO/FEDER,UE), and by the Icrea Academia Award.

Scenes with back-light illumination are problematic when captured with a typical LDR camera, as they result in dark regions where details are not perceivable. In this paper, we present a method that, given an LDR backlit image, outputs an image where the information that was not visible in the dark regions is recovered without losing information in the already well-exposed parts of the image. Our method has three main steps: first, a variational model is minimized using gradient descent, and the iterates of the minimization are used to obtain a set of weight maps. Second, we consider the tone-mapping framework [3] that depends on four parameters. Two different sets of parameters are learned by dividing the image in the darker and lighter parts. Then, we interpolate the two sets of parameter values in as many sets as weighting maps, and tone-map the original image with each set of parameters. Finally, we merge the new tone-mapped images depending on the weighting maps. Results show that our method outperforms current backlit image enhancement approaches both quantitatively and qualitatively.