Biologically inspired image enhancement based on Retinex

Abstract

This paper presents a biologically inspired adaptive image enhancement method, consisting of four stages: illumination estimation, reflection extraction, color restoration and postprocessing. The illumination of the input image is estimated using guided filter. We propose to utilize the smoothed Y channel in the YCbCr color space as the guidance image, since it can better capture the illuminance of the real scene. The reflection of the input image is extracted using the Retinex algorithm and refined through color restoration. In order to further improve the quality of the extracted reflection, we explore a learning strategy to select the optimal parameters of the nonlinear stretching by optimizing a novel image quality measurement, named as the Modified Contrast–Naturalness–Colorfulness (MCNC) function. Compared with the original CNC function, the proposed MCNC function employs a more effective objective criterion and can better agree with human visual perception. Both qualitative and quantitative experiments demonstrate that the proposed method is adaptive and robust to outdoor images and achieves favorable performance against state-of-the-art methods especially for images captured under extremely hazed or low-light conditions.