Blind Inverse Gamma Correction with Maximized Differential Entropy

TL;DR

This paper introduces an efficient, mathematically concise method for blind inverse gamma correction based on maximizing differential entropy, improving image quality across various applications without arbitrary tuning parameters.

Contribution

The paper proposes a novel adaptive gamma transformation method (AGT-ME) derived from a maximized differential entropy model, with a modified version for better visual perception, outperforming existing methods.

Findings

Accurately estimates gamma distortion in the range 0.1 to 3.0

Outperforms state-of-the-art gamma correction methods

Effective in applications like image enhancement and profilometry

Abstract

Unwanted nonlinear gamma distortion frequently occurs in a great diversity of images during the procedures of image acquisition, processing, and/or display. And the gamma distortion often varies with capture setup change and luminance variation. Blind inverse gamma correction, which automatically determines a proper restoration gamma value from a given image, is of paramount importance to attenuate the distortion. For blind inverse gamma correction, an adaptive gamma transformation method (AGT-ME) is proposed directly from a maximized differential entropy model. And the corresponding optimization has a mathematical concise closed-form solution, resulting in efficient implementation and accurate gamma restoration of AGT-ME. Considering the human eye has a non-linear perception sensitivity, a modified version AGT-ME-VISUAL is also proposed to achieve better visual performance. Tested on variable datasets, AGT-ME could obtain an accurate estimation of a large range of gamma distortion (0.1 to 3.0), outperforming the state-of-the-art methods. Besides, the proposed AGT-ME and AGT-ME-VISUAL were applied to three typical applications, including automatic gamma adjustment, natural/medical image contrast enhancement, and fringe projection profilometry image restoration. Furthermore, the AGT-ME/ AGT-ME-VISUAL is general and can be seamlessly extended to the masked image, multi-channel (color or spectrum) image, or multi-frame video, and free of the arbitrary tuning parameter. Besides, the corresponding Python code (https://github.com/yongleex/AGT-ME) is also provided for interested users.