Super-resolution image processing with total variation regularization and shock filters

Multi-frame super-resolution image reconstruction aims to restore a highresolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback-Leibler divergence. In this way, the missing information is estimated simultaneously with the B Shengrong Zhao Circuits Syst Signal Process high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.

show abstract

“…The traditional TV model [25], which preserves the edges of the image, is the most widely used regulation term.…”

Section: Prior Distributionsmentioning

confidence: 99%

“…In fact, many effective prior models have been proposed, such as the total variation (TV) model [25], the simultaneous autoregressive (SAR) model [14], the 1 model [22], and the compound model [20]. Although these prior models are able to preserve the detailed information of the images, they cannot estimate missing information effectively.…”

mentioning

confidence: 99%