Image enhancement revisited: From first order to second order statistics
Xiao Shu, Xiaolin Wu · 2013
This paper proposes a new image enhancement algorithm in a recently published framework of optimal contrast-tone mapping (OCTM). The new algorithm represents a fundamental departure from traditional histogram-based image enhancement techniques (i.e., histogram equalization and all of its variants), in that second-order rather than first-order statistics is used. Perceptual quality attributes, such as contrast and tone, are quantified by joint distribution of the values of spatially adjacent pixels instead of histograms as of today. The problem of image enhancement is then formulated as one of linear programming, at the heart of which is a joint distribution-based objective function that can accommodate various psychovisual properties related to image quality. The new linear program algorithm for image enhancement is implemented and its superior performance in visual quality is empirically verified, corroborating with our analysis.