Performance comparison of hybrid wavelet transform-I variants and contrast limited adaptive histogram equalization combination for image enhancement

Abstract

Image enhancement is one of the important part of image processing. Proposed research presents an image enhancement method, named CLAHE-HWT derived from its existing CLAHE-DWT, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) with Hybrid Wavelet Transform Type I (HWT I) wavelets. The method includes, the original image is decomposed into low-frequency and high-frequency components by HWT I. Then, the low-frequency coefficients is enhanced using CLAHE and the high-frequency coefficients kept unchanged to limit noise enhancement. Finally, the image is reconstructed by taking inverse HWT of the new coefficients. In order to counteract over-enhancement, the recreated and original images are averaged using an originally proposed weighting factor. Two orthogonal transforms combine to form a hybrid wavelet. Here different orthogonal transforms are used like Kekre, Walsh, Cosine, Hartley and Haar in 5 × 4 combinations total 20 hybrid wavelets of type I. This research compares all the 20 combinations of HWT I to find out the best combination of HWT with CLAHE. Experimental results demonstrate CLAHE-HWT shows better results for noise depression and avoid over enhancement.