Subjective evaluations of example-based, total variation, and joint regularization for image processing

This is the copy of journal's version originally published in Proc. SPIE 8296: http://dx.doi.org/10.1117/12.917710. Reprinted with permission of SPIE.We report on subjective experiments comparing example-based regularization, total variation regularization, and the joint use of both regularizers. We focus on the noisy deblurring problem, which generalizes image superresolution and denoising. Controlled subjective experiments suggest that joint example-based regularization and total variation regularization can provide subjective gains over total regularization alone, particularly when the example images contain similar structural elements as the test image. We also investigate whether the regularization parameters can be trained by cross-validation, and we compare the reconstructions using crossvalidation judgments made by humans or by fully automatic image quality metrics. Experiments showed that of five image quality metrics tested, the structural similarity index (SSIM) correlates best with human judgement of image quality, and can be profitably used to cross-validate regularization parameters. However, there is a significant quality gap between images restored using human or automatic parameter cross-validation

Subjective evaluations of example-based, total variation, and joint regularization for image processing

We report on subjective experiments comparing example-based regularization, total variation regularization, and the joint use of both regularizers. We focus on the noisy deblurring problem, which generalizes image superresolution and denoising. Controlled subjective experiments suggest that joint example-based regularization and total variation regularization can provide subjective gains over total regularization alone, particularly when the example images contain similar structural elements as the test image. We also investigate whether the regularization parameters can be trained by cross-validation, and we compare the reconstructions using crossvalidation judgments made by humans or by fully automatic image quality metrics. Experiments showed that of five image quality metrics tested, the structural similarity index (SSIM) correlates best with human judgement of image quality, and can be profitably used to cross-validate regularization parameters. However, there is a significant quality gap between images restored using human or automatic parameter cross-validation