A Performance Comparison of Single Image Reconstruction Techniques under Several Noisy Environments

Due to noise contamination on the image during the observation process, digital image reconstruction is an essential in terms of recovering the information of the contents (e.g. document and image) and utilized in many applications such as digital image forensic, medical image processing, machine vision, and etc. Therefore, this paper is concerned with the performance comparisons of single image employing various reconstruction approaches. These are Inverse filter, Wiener filter, Regularized technique, Lucy-Richardson technique, and Bayesian technique based on median, mean, myriad, and meridian filters. The experiments test on the three standard pictures (Lena, Resolution chart, and Susie (40th)) under the same noise conditions. Four types of noise models consider in this paper are AWGN, Poisson, Salt&Pepper, and Speckle noises. The performance of evaluations is done by varying parameters of individual technique. Peak-signal-to-noise-ratio (PSNR) is a key indicator on the performance comparison results.