A segmentation and mixing strategy for retinex based image enhancement

Based on the human visual system, the Retinex image enhancement algorithm offers a forceful improvement for nonuniformly illuminated images. By observing that the original nonuniformly illuminated image has good performance in the bright area and the Retinex improved image has outstanding performance in the dark area, algorithms aiming to integrate the merits from both images were proposed. In this paper, we proposed an algorithm which performs the wavelet transform (WT) before segmentation and mixing. The wavelet transform decomposes an image into the coarse component and the detail components. The segmentation is performed on the course component and the result is used to fuse the course and detail components from both sources. The proposed algorithm is first subjectively assessed by using the dynamic range independent image quality assessment metric (DRIM) and compared with three existing algorithms. Experimental results show that the improved images using our proposed algorithm reveal significant amplification of contrast in the dark areas and mild loss and reversal of contrast in other areas. The proposed algorithm is objectively assessed by using information entropy. Experimental results show that among the four methods compared the information entropy of our proposed algorithm is the highest.