Adaptive Region Selection and Adaptive Regularization for Image Deblurring and Super-Resolution Processing

In this paper, we present an approach based on adaptive region selection and adaptive regularization for image deblurring and super-resolution processing. This method has been experimentally validated as effective. Through adaptive region selection, which typically involves implementing region detection algorithms using techniques like edge detection or saliency mapping, we can precisely identify areas requiring processing to achieve superior deblurring results. The adaptive regularization component employs spatially-varying regularization parameters, often implemented through weighted Total Variation (TV) norms or patch-based priors, enabling more refined image processing for higher resolution enhancement. These techniques have been practically verified and demonstrated to yield significant improvements in image quality. A typical implementation would involve iterative optimization algorithms like ADMM (Alternating Direction Method of Multipliers) or gradient descent methods with adaptive parameter updates at each iteration.