Reflectance-Guided, Contrast-Accumulated Histogram Equalization
This work addresses image enhancement for applications requiring better visibility in non-uniform illumination, but it appears incremental as it builds on existing histogram equalization techniques.
The paper tackles the problem of simultaneously improving global and local image contrast in image enhancement by proposing a histogram equalization-based method that adaptsively adjusts brightness and reveals hidden details without losing global contrast.
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-dependent requirements of brightness enhancement and improves the visibility of details without losing the global contrast. This method incorporates the spatial information provided by image context in density estimation for discriminative histogram equalization. To minimize the adverse effect of non-uniform illumination, we propose defining spatial information on the basis of image reflectance estimated with edge preserving smoothing. Our method works particularly well for determining how the background brightness should be adaptively adjusted and for revealing useful image details hidden in the dark.