Application of evolutionary programming to adaptive regularization in image restoration

Image restoration is a difficult problem due to the ill-conditioned nature of the associated inverse filtering operation, which requires regularizatio n techniques. The choice of the corresponding regularization parameter is t hus an important issue since an incorrect choice would either lead to noisy appearances in the smooth regions or excessive blurring of the textured re gions. In addition, this choice has to be made adaptively across different image regions to ensure the best subjective quality for the restored image. In this paper, we employ evolutionary programming (EP) to solve this adapt ive regularization problem by generating a population of potential regulari zation strategies, and allowing them to compete under a new error measure w hich characterizes a large class of images in terms of their local correlat ional properties. The nonavailability of explicit gradient information for this measure motivates the adoption of EP techniques for its optimization, which allows efficient search at multiple error surface points. The adoptio n of EP also allows the broadening of the range of possible cost functions for image processing so that we can choose the most relevant function rathe r than the most tractable one for a particular image processing application .