Generative Models for Multi-Illumination Color Constancy

Das, Partha; Liu, Yang; Karaoğlu, Sezer; Gevers, Theo

doi:10.1109/iccvw54120.2021.00139

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…These methods require to detect grey surfaces [10], specularities [11] or to segment the images [12]. The third category contains all the learning-based approaches based on Gamut Mapping [13,14], patch-based approaches [15] or the deep networks solutions [1,2,3,16]. In this paper, we propose to take advantage and extend these last solutions so that they are able to predict the colors of a set of reference patches, and not only the color of a grey surface.…”

Section: Related Workmentioning

confidence: 99%

“…Computational color constancy tries to mimic this behaviour with computer vision systems. Given a digital image, the idea is first to estimate the color of the illumination and then to remove the impact of this illumination from the pixel colors [1,2,3,4]. In this paper, we propose to improve these two steps with an original and simple solution.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Colors of Reference Surfaces for Color Constancy

Dubuisson

Muselet

Basso-Bert

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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The classical color constancy algorithms concentrate only on the color of a grey surface to estimate the light color and to white balance the image. In this paper, we show that the quality of the whole process can be clearly improved by predicting and correcting the colors of a set of reference surfaces. The ground truth of the surface color under white light can be easily obtained with a set of images acquired by the considered camera under sun light. Thus, we design a deep network to predict the colors of the reference surfaces of a color checker as if it had been in the scene at acquisition time. We show that our solution improves the two steps of the color constancy process on 9 datasets and we claim that being able to synthetically insert a color chart in any image can help for many other tasks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the Colors of Reference Surfaces for Color Constancy

Dubuisson

Muselet

Basso-Bert

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

“…Recently, artificial intelligence (AI) methods have enabled more versatile, optimized, robust and fast algorithms to perform color constancy 14,15 . Within this context, DermoCC‐GAN (Dermatological Color Constancy Generative Adversarial Network), the first AI method in the literature that performs a color constancy task on dermatoscopic images as an image‐to‐image translation problem with a Generative Adversarial Network (GAN), was developed 15 .…”

Section: Introductionmentioning

confidence: 99%

“…Recently, artificial intelligence (AI) methods have enabled more versatile, optimized, robust and fast algorithms to perform color constancy. 14,15 Within this context, DermoCC-GAN (Dermatological Color Constancy Generative Adversarial Network), the first AI method in the literature that performs a color constancy task on dermatoscopic images as an image-to-image translation problem with a Generative Adversarial Network (GAN), was developed. 15 The use of a color constancy method as a preprocessing step has been widely demonstrated in the literature to improve the performance of AI, and specifically deep learning, methods for classification and/or segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study

Branciforti,

Meiburger,

Zavattaro

et al. 2023

Skin Research and Technology

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BackgroundThe quality of dermoscopic images is affected by lighting conditions, operator experience, and device calibration. Color constancy algorithms reduce this variability by making images appear as if they were acquired under the same conditions, allowing artificial intelligence (AI)‐based methods to achieve better results. The impact of color constancy algorithms has not yet been evaluated from a clinical dermatologist's workflow point of view. Here we propose an in‐depth investigation of the impact of an AI‐based color constancy algorithm, called DermoCC‐GAN, on the skin lesion diagnostic routine.MethodsThree dermatologists, with different experience levels, carried out two assignments. The clinical experts evaluated key parameters such as perceived image quality, lesion diagnosis, and diagnosis confidence.ResultsWhen the DermoCC‐GAN color constancy algorithm was applied, the dermoscopic images were perceived to be of better quality overall. An increase in classification performance was observed, reaching a maximum accuracy of 74.67% for a six‐class classification task. Finally, the use of normalized images results in an increase in the level of self‐confidence in the qualitative diagnostic routine.ConclusionsFrom the conducted analysis, it is evident that the impact of AI‐based color constancy algorithms, such as DermoCC‐GAN, is positive and brings qualitative benefits to the clinical practitioner.

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“…In Refs. 32 and 33, researchers built networks that can be used for white balance of light sources under multiple light sources. Wang and Wang 34 applied the algorithm of color constancy to the processing of medical images and proposed an architecture based on full convolution network to process color microscopic images under uneven illumination, which significantly improved the visual perception of images.…”

Section: Introductionmentioning

confidence: 99%

Evolving convolution neural network by optimal regularization random vector functional link for computational color constancy

2022

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. To automatically eliminate the influence of external illumination on the acquired image, restore the true color information of the object, and provide true and stable color features for computer vision tasks, our work is based on the convolutional neural network (CNN) VGG19 for feature extraction of image information. Moreover, it proposes a model based on atom search optimization improved by chaotic-logistic maps to optimize the regularization random vector functional link (RRVFL). The optimized RRVFL replaces CNN’s regression layer to estimate the image illumination and then restore the image. First, we used chaotic-logistic maps to optimize the initial value of search agent of the atom search optimization (CASO) algorithm and then used the search agent segment mapping method proposed in our study to simultaneously optimize the number of nodes in hidden layer, regularization factors, input weight, and the bias of RRVFL hidden layer. This avoids the problem of serious fluctuation of prediction results and low accuracy caused by the randomness of the RRVFL parameters. After the predicted illumination information was obtained with the optimized RRVFL, the image was restored using the diagonal transformation method. Comparative experiments showed that the average angular errors of the illumination estimation of the V19-CASO-RRVFL algorithm proposed in our study were 1.73, 0.30, 0.3406, and 0.0976 lower than those of the gray-edge-2, GE-CASO-RRVFL, DS-Net, and color constancy on deep residual learning algorithms, respectively.

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Generative Models for Multi-Illumination Color Constancy

Cited by 9 publications

References 29 publications

Predicting the Colors of Reference Surfaces for Color Constancy

Predicting the Colors of Reference Surfaces for Color Constancy

Impact of artificial intelligence‐based color constancy on dermoscopical assessment of skin lesions: A comparative study

Evolving convolution neural network by optimal regularization random vector functional link for computational color constancy

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