Digital Image Restoration
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Handbook of Image and Video Processing (Communications, Networking and Multimedia)
Estimation of structural information content in images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Accurate image restoration is of paramount importance for low-level vision, computer vision, and various other fields. Numerous complex restoration algorithms have been stated in the literature. Performance of these restoration algorithms differs with the nature of the image and distortion. These algorithms are evaluated either qualitatively or quantitatively by comparing the restored image with the original image. The practical drawback of this quantitative comparison is the requirement for the original image. In this paper, we propose a measure, with theoretical grounding, to objectively evaluate the restoration algorithms with no knowledge about the original image. This measure analyses the deblurring as well as the denoising nature of the restoration method. The main utility of this measure will be in designing automatic restoration systems. Effectiveness of this measure is substantiated with experimental results.