Edge-preserving image restoration using adaptive constrained optimization

In general, image restoration problems are ill-posed and need to be regularized. A difficult task in image regularization is to avoid smoothing of image edges. We propose a new edge-preserving image restoration algorithm using adaptive constrained optimization. In order to exploit the local image characteristics efficiently, we classify image blocks into edge and non-edge blocks. We then apply an adaptive constrained least squares (CLS) algorithm to eliminate noise around the edges. Experimental results demonstrate that the proposed algorithm produces perceptually improved image quality