Adaptive Image Enhancement Based On The Retinex Theory

The quality of images captured outdoor is mainly affected by environmental effects, such as low illumination or clouds and fog, which make image quality dim and blurry, color fad-ed. Therefore, using image processing techniques to enhance and restore degenerated images properly, is a necessary way to improve the utilization rate of image information.This paper intensively studies the Retinex theory based on the color constancy. Aiming at improve ne deficiencies of the Multi-scale Retinex with Color Restoration (MSRCR), such as too much parameters and producing color saturation easily, this paper makes some improve-ments and proposes an adaptive image enhancement method based on the Retinex theory.The algorithm presented in the paper consists of four stages:illumination estimation, re-flection extraction, color restoration and postprocessing. Firstly, estimate the illumination of the input image by employing the guided filter. In order to better capture the illuminance of the real scene and inhibit pseudo halos, this paper proposes to utilize the smoothed Y channel in the YCbCr color space as the guidance image, where the smoothing operation is conducted by the multi-scale Gaussian filters with different weights. Then, the reflection of the input image is ex-tracted using the Retinex algorithm and refined through color restoration. Lastly, a kind of post-processing is proposed to further improve the visual quality of enhanced results. Specifically, the postprocessing combines the stretching based on the histogram distribution and gamma correc-tion, whose optimal parameters are learned by utilizing the Quantum-behaved Particle Swarm Optimization (QDPSO) method. Moreover, both the contrast and the color quality have great influence on human subjective feeling. Therefore, a novel image quality measurement, named as the Modified Contrast-Naturalness-Colorfulness (MCNC) function, is proposed as the objective function of optimization process. Compared with the CNC (Contrast-Naturalness-Colorfulness) function, the proposed MCNC function employs a more effective objective criterion on the con-trast assessment and makes enhanced results better agree with human vision.Both qualitative and quantitative experiments demonstrate that the proposed method is adaptive and robust to outdoor images and achieves favorable performance on improving con-trast and color restoration against state-of-the-art methods, especially for images captured under extremely hazed or low-light conditions.