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Found 6 papers, 1 papers with code
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Although some recent works focused on the differentiation of details and artifacts, this is a very challenging problem and a satisfactory solution is yet to be found.
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback
This strategy leverages the high-quality image generation capabilities of DMs, while recognizing the importance of obtaining a single trustworthy solution, especially in use cases, such as identification of specific digits or letters, where generating multiple feasible solutions may not lead to a reliable outcome.
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors
As a result, the performance of an SR model varies noticeably from image to image over a test set depending on whether characteristics of specific images are similar to those in the training set or not.
Perception-Distortion Trade-off in the SR Space Spanned by Flow Models
We achieve this by benefiting from a diverse set of feasible photo-realistic solutions in the SR space spanned by flow models.
Two-stage domain adapted training for better generalization in real-world image restoration and super-resolution
It is well-known that in inverse problems, end-to-end trained networks overfit the degradation model seen in the training set, i. e., they do not generalize to other types of degradations well.
On the Computation of PSNR for a Set of Images or Video
Others compute a single PSNR from the arithmetic mean of frame MSEs for each video.
