基于颜色恒常性的低照度图像视见度增强

在彩色成像过程中,低照度是导致图像降质的一个重要因素. 本文提出了一种新的基于颜色恒常性的低照度图像视见度增强算法. 为了避免场景光源的影响,提出了像素有效集的概念. 基于灰色调算法的灰度像素假设,利用有效像素估计光 照的颜色;在后处理阶段,利用有效像素的灰度级范围确定直方图剪裁的上下限. 实验表明,算法有效地校正了图像 的颜色、对比度和亮度,从而增强了图像的视见度,且不会产生Retinex 算法所固有的灰化效应和Halo 效应.

Color Constancy-based Visibility Enhancement of Color Images inLow-light Conditions

Color imaging in low-light conditions is often significantly degraded by insufficient lighting and color cast. In this paper, we present a novel color constancy-based method to enhance the visibility of low-light images. Due to the occurrence of the scene illuminant or specular highlights, inhomogeneous illumination affects the estimation of the illuminant color across the scene. Thus in the image, an active set of pixel values is defined to reduce their effects. We then propose an improvement on the gray-pixel assumption imposed by the shades-of-gray algorithm using the active set. The improved shades-of-gray algorithm is applied to low-light image visibility enhancement for cast removal. Limits of histogram clipping are found in the active set in the post-processing step that is added to render the resulting image with a good lightness and contrast range. Results on a variety of low-light images demonstrate that the proposed method can achieve a good correction for lightness, contrast and color fidelity without local graying-out artifacts or halo artifacts intrinsic to Retinex approaches.