DDCATNet: Effective Deep Learning-Based Illumination Color Cast Estimation Approach for Achieving Computational Color Constancy [PDF]
SensorsDigital camera sensors are designed to capture a wide range of incident illuminants, enabling the creation of high-quality images. However, these sensors lack the capability to differentiate between the color of the source illuminant and the actual color
Ho-Hyoung Choi
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Deep Learning-Based Computational Color Constancy With Convoluted Mixture of Deep Experts (CMoDE) Fusion Technique [PDF]
IEEE Access, 2020 In the human and computer vision, color constancy is the ability to perceive the true color of objects in spite of changing illumination conditions. Color constancy is remarkably benefitting human and computer vision issues such as human tracking, object
Ho-Hyoung Choi, Byoung-Ju Yun
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Sensor sharpening for computational color constancy [PDF]
Journal of the Optical Society of America A: Optics and Image Science, and Vision, 2001 Sensor sharpening [J. Opt. Soc. Am. A 11, 1553 (1994)] has been proposed as a method for improving computational color constancy, but it has not been thoroughly tested in practice with existing color constancy algorithms. In this paper we study sensor sharpening in the context of viable color constancy processing, both theoretically and empirically ...
Kobus Barnard, Brian Funt
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Spectral Sharpening of Color Sensors: Diagonal Color Constancy and Beyond
Sensors, 2014 It has now been 20 years since the seminal work by Finlayson et al. on the use of spectral sharpening of sensors to achieve diagonal color constancy. Spectral sharpening is still used today by numerous researchers for different goals unrelated to the ...
Javier Vazquez-Corral +1 more
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Approaching the computational color constancy as a classification problem through deep learning [PDF]
Pattern Recognition, 2017 Computational color constancy refers to the problem of computing the illuminant color so that the images of a scene under varying illumination can be normalized to an image under the canonical illumination. In this paper, we adopt a deep learning framework for the illumination estimation problem.
Seon Joo Kim, Seoung Wug Oh
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Computational color constancy using chromagenic filters in color filter arrays [PDF]
Proceedings of SPIE, 2012 This is the copy of journal's version originally published in Proc. SPIE 8298: http://dx.doi.org/10.1117/12.912073. Reprinted with permission of SPIE.
Raju Shrestha, Jon Yngve Hardeberg
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Deep Learning Network with Illuminant Augmentation for Diabetic Retinopathy Segmentation Using Comprehensive Anatomical Context Integration [PDF]
DiagnosticsBackground/Objectives: Diabetic retinopathy (DR) segmentation faces critical challenges from domain shift and false positives caused by heterogeneous retinal backgrounds.
Sakon Chankhachon +3 more
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IEEE Access, 2021 For the imaging signal processing (ISP) pipeline of digital image devices, it is of high significance to remove undesirable illuminant effects and obtain color invariance, commonly known as ‘computational color constancy’.
Ho-Hyoung Choi, Byoung-Ju Yun
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Three-Color Balancing for Color Constancy Correction
Journal of Imaging, 2021 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.
Teruaki Akazawa +3 more
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Computational modeling of color perception with biologically plausible spiking neural networks.
PLoS Computational Biology, 2022 Biologically plausible computational modeling of visual perception has the potential to link high-level visual experiences to their underlying neurons' spiking dynamic. In this work, we propose a neuromorphic (brain-inspired) Spiking Neural Network (SNN)-
Hadar Cohen-Duwek +2 more
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