Search Results for author:
Found 9 papers, 3 papers with code
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution
Recovering high-frequency details and textures from low-resolution images remains a fundamental challenge in super-resolution (SR), especially when real-world degradations are complex and unknown.
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution
This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
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.
