Incorporating local statistics in image error measurement for adaptive image restoration

Abstract

This paper presents an image restoration technique incorporating local statistical knowledge in the cost function. Instead of using a conventional grayscale-based error measurement such as the mean squared error, we compare local statistical information about regions in two images using a new error measure. Transient features such as edges and textures are more strongly emphasized than relatively homogeneous regions. With the addition of this local information, we attempt to provide a measure closer to human visual appraisal. We then extend the popular constrained squared-error cost function by incorporating this image error measure. Due to its nonlinear nature, conventional restoration algorithms cannot optimize this cost function efficiently. Therefore we seek an iterative approach. In particular, an extended neural network algorithm is proposed to perform the restoration. It is shown that this technique is efficient, effective, and robust. It compares favorably with other techniques when applied to both grayscale and color images. The results of a subjective survey comparing the proposed algorithm with a more conventional neural network algorithm are presented. The subjects tested in the survey overwhelmingly favored the results provided by the proposed method.