Reflectance-Guided Histogram Equalization and Comparametric Approximation

Xiaomeng Wu, Takahito Kawanishi, Kunio Kashino

Abstract: Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this issue, we propose a histogram equalization-based method called RG-CACHE. It adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. RG-CACHE incorporates the spatial information provided by image context into density estimation for discriminative histogram equalization. To minimize the adverse effect of nonuniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge-preserving smoothing. RG-CACHE works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark. To handle the loss of details due to the monotonicity of the intensity mapping function, we further propose a post-processing method to approximate RG-CACHE with a brightness transformation function corresponding to a parameterized camera response function. This method is called comparametric approximation. It takes into account a regression problem, in which the parameters of the camera response function are chosen so that the converted intensities are optimally matched to the image enhanced by RG-CACHE. Comparametric approximation is especially suitable for recovering useful image details that tend to be suppressed due to insufficient reflectance contrast.