Color Constancy with Spatio-Spectral Statistics

Abstract

Abstract-We introduce an efficient maximum likelihood approach for one part of the color constancy problem: removing from an image the color cast caused by the spectral distribution of the dominating scene illuminant. We do this by developing a statistical model for the spatial distribution of colors in white balanced images (i.e. those that have no color cast), and then using this model to infer illumination parameters as those being most likely under our model. The key observation is that by applying spatial band-pass filters to color images one unveils color distributions that are unimodal, symmetric, and well-represented by a simple parametric form. Once these distributions are fit to training data, they enable efficient maximum likelihood estimation of the dominant illuminant in a new image, and they can be combined with statistical prior information about the illuminant in a very natural manner. Experimental evaluation on standard datasets suggests that the approach performs well.

Illuminant estimation in multispectral imaging

“…The decision for illuminant color is based on minimum error between the estimation in consecutive scales. CCC based on spatio-temporal statistics in a scene was proposed by Chakrabarti et al [37], where the spatial features of object surfaces are also accounted for in the determination of the illuminant. That work is improved in Ref.…”

A Hybrid Strategy for Illuminant Estimation Targeting Hard Images

“…Statistical-based methods including: S1 = shades of grey [13], S2 = grey world [4], S3 = 1 st order grey edge [27], S4 = 2 nd order grey edge and S5 = white-patch [25]. Learning-based methods including: L1 = exemplar-based [23], L2 = color constancy using natural image statistics [16], L3 = edgebased gamut, L4 = pixel-based gamut, L5 = intersectionbased gamut [14,18], L6 = Bayesian method [15] and L7 = spatial correlation [5].…”

“…Their performance, however, is generally not as good as learning-based methods. Learning-based methods (representative examples include [14,12,16,15,18,5,11,23]) exploit the availability of training images that have labelled ground truth illumination. These methods use image features to train regressors to predict the illumination based on the input image and associated training-data.…”

Integrated Saturation Weighting based Color Cat Algorithm

“…Image form a contrast enhancement [7] and is really a classical difficulty in photograph processing and computer imaginative and prescient vision. Image enhancement is known as some sort of preprocessing part of many locations like video/image control applications and talks recognition, texture synthesis etc.…”