Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) for Enhancement of Multimodal Biometric Images

Abstract

The research work proposes a novel triangular fuzzy membership (TFM) function-based contrast limited adaptive histogram equalization (CLAHE) for biometric image enhancement. Biometric images have wide applications in the areas of verification and authentication systems. For accurate identification and verification, pre-processing of captured biometric images becomes essential. When the region of interest is smaller than the original image, a variation of histogram equalization called adaptive histogram equalization (AHE) is used. AHE enhances contrast of images by considering local regions. Along with local contrast, noise in those regions also get amplified by using AHE. This amplification of noise can be resolved by applying a contrast limited AHE (CLAHE) which limits the contrast in the enhanced local regions by clipping the histogram at a pre-fixed limit. CLAHE yields good results by limiting the contrast and enhancing local regions, but it is image invariant since it uses pre-determined clip limit for limiting contrast. The proposed research work TFM-CLAHE puts forward the idea of image variant, automatic clip value determinant algorithm for enhancement. The algorithm employs triangular fuzzy membership function to determine clip-limit and limits contrast by clipping the histogram at the computed clip-level. TFM function computes the clipping parameter by considering intensities of pixels. The computed fuzzy clip-limit overrides the pre-defined limit. Consequently, the clipping parameter varies according to the image under consideration and yields better enhancement results. The proposed work is experimented on multimodal biometric images acquired from Chinese Academy of Science, Institute of Automation Iris, Face and Fingerprint databases. TFM-CLAHE computes appropriate clipping limit for each of these heterogenous images. The results of the proposed work are evaluated on the grounds of images’ average information content, mean square error, peak signal noise ratio, natural image quality evaluator, no-reference free energy based robust metric, blind image quality measure of enhanced images and no reference quality metric for contrast distortion. The results show good enhancement and these are compared with existing conventional image enhancement techniques.