An iterative image enhancement algorithm and a new evaluation framework

Image enhancement is important for images captured in low contrast and low illumination conditions. In this study, we propose a new iterative algorithm for image enhancement based on analysis on embedded surfaces of images. In our method, scaled surface area and the surface volume are proposed and used to reconstruct the image iteratively for contrast enhancement, and the illumination of the reconstructed image can also be adjusted simultaneously. On the other hand, the most common methods for measuring the quality of enhanced images mean square error (MSE) or peak signal-to-noise-ratio (PSNR) have been recognized as inadequate measures because they do not evaluate the result in the way that the human vision system does. This paper also presents a new framework for evaluating image enhancement using both objective and subjective measures. This framework can also be used for other image quality evaluations such as denoising evaluation. We compare our enhancement method with some well-known enhancement algorithms, including wavelet and curvelet methods, using the new evaluation framework. The results show that our method gives better performance in most objective and subjective criteria than the conventional methods.