text: Abstract 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. The proposed method works under the assumption of uniform illumination over the scene and aims for the accurate illuminant color computation. Specifically, we trained the convolutional neural network to solve the problem by casting the color constancy problem as an illumination classification problem. We designed the deep learning architecture so that the output of the network can be directly used for computing the color of the illumination. Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets.
  @cite_19: The aim of computational color constancy is to estimate the actual surface color in an acquired scene disregarding its illuminant. Many solutions try to first estimate the illuminant and then correct the image with the illuminant estimate. Based on the linear image formation model, we propose in this work a new strategy to estimate the illuminant. Inspired by the feedback modulation from horizontal cells to the cones in the retina, we first normalize each local patch with its local maximum to obtain the so-called locally normalized reflectance estimate (LNRE). Then, we experimentally found that the ratio of the global summation of true surface reflectance to the global summation of LNRE in a scene is approximately achromatic for both indoor and outdoor scenes. Based on this substantial observation, we estimate the illuminant by computing the ratio of the global summation of the intensities to the global summation of the locally normalized intensities of the color-biased image. The proposed model has only one free parameter and requires no explicit training with learning-based approach. Experimental results on four commonly used datasets show that our model can produce competitive or even better results compared to the state-of-the-art approaches with low computational cost.
  @cite_1: The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of @math or @math . Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.
summary: Recently, understanding the mechanism of the human visual system (HVS) has been found to be useful in building the statistical assumptions by mimicking the human built-in ability of color constancy @cite_19 @cite_1 . One limitation of these approaches is that we are still far from fully understanding the mechanism of the HVS. In this regard, we believe our deep learning approach which train HVS inspired complex model (CNN) with large data to simulate color information processing on the human brain could make a breakthrough for the problem of the computational color constancy because we do not need to know how it works specifically.