English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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 imagesuperresolution and denoising. Controlled subjective experiments suggest that joint example-based regularizationand total variation regularization can provide subjective gains over total regularization alone, particularly whenthe example images contain similar structural elements as the test image. We also investigate whether theregularization parameters can be trained by cross-validation, and we compare the reconstructions using crossvalidationjudgments made by humans or by fully automatic image quality metrics. Experiments showed that offive image quality metrics tested, the structural similarity index (SSIM) correlates best with human judgementof image quality, and can be profitably used to cross-validate regularization parameters. However, there is asignificant 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