A New Method for Image Contrast Enhancement Based on Automatic Specification of Local Histograms

The histogram equalization (HE) method is widely used for image contrast enhancement. While it can enhance the overall contrast, the inherent dependence of its transformation function on the global content of the image limits its ability to enhance local details at the same time. Furthermore, using the method to reform the image histogram into a uniform one usually results in a significant change in the image brightness and saturation artifacts, specifically in low contrast images. One extension for HE is the local histogram equalization (LHE) method that processes the image on block-by-block basis and uses the transformation function of HE for that block to modify its center pixel. Although the LHE method can enhance image details, it often causes unacceptable and unnatural image modification due to noise amplification, especially in smooth regions. In this paper, we propose a new local enhancement method referred as Automatic Local Histogram Specification (ALHS). The ALHS method is applied locally such that for each pixel in the image a neighborhood/block of specific size is defined with that pixel being at the center of the block. Next, the ALHS method modifies the graylevel value of this central pixel by specifying an output histogram and applying the histogram matching algorithm. The core idea of the ALHS method is specifying the best output histogram for the block associated with each pixel. To specify the output histogram, a minimization problem for a functional with a constraint that preserves the mean brightness of that block is solved. The specified histogram in the ALHS method provides the maximum graylevel stretching and preserves the mean brightness of the block. This is reflected on the processed image by the enhancement of its contrast, preservation of its outlook, and minimum introduction of noise and overenhancement artifacts. The ALHS method is fully automatic and provides an analytic solution for the output histogram as a function of the mean brightness of the block. Our experimental evaluation on a set of benchmark images involved the use of two quantitative measures and visual assessment. The evaluation results show that the ALHS method outperforms both the HE and LHE methods.