A general histogram modification framework for efficient contrast enhancement

Ke Gu, Guangtao Zhai, Shiqi Wang, Min Liu, Jiantao Zhou, Weisi Lin

Abstract: In this paper we propose a new general histogram modification framework for contrast enhancement. The proposed model works with a hybrid transformation technique to improve image brightness and contrast based on an optional histogram matching in terms of reassigned probability distribution and S-shaped transfer mapping. Experimental results conducted on natural, dimmed, and tone-mapped images show that the proposed technique creates enhanced images efficiently with equivalent or superior visual quality to those produced by classical and state-of-the-art enhancement approaches.