Enhancement of low exposure images via recursive histogram equalization algorithms

This paper proposes two exposure based recursive histogram equalization methods for image enhancement. The proposed methods are very effective for images acquired in low light condition like underwater sequences or night vision images. The first method is recursive exposure based sub-image histogram equalization (R-ESIHE) that recursively performs ESIHE [20] method till the exposure residue among successive iteration is less than a predefined threshold. The second method is named as recursively separated exposure based sub image histogram equalization (RS-ESIHE) that performs the separation of image histogram recursively; separate each new histogram further based on their respective exposure thresholds and equalize each sub histogram individually. The experimental results show that low exposure image enhancement problem was not addressed by earlier HE based methods, has been efficiently handled by these new methods. The performance evaluation of new methods is done in terms of image information content as well as visual quality inspection. The proposed methods outperforms earlier HE based contrast enhancement algorithms specifically for low light images.