SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 1721–1730 of 3874 papers

TitleStatusHype
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Dense U-net for super-resolution with shuffle pooling layer—0
Densely Connected High Order Residual Network for Single Frame Image Super Resolution—0
Blaze3DM: Marry Triplane Representation with Diffusion for 3D Medical Inverse Problem Solving—0
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution—0
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution—0
Dense Dual-Attention Network for Light Field Image Super-Resolution—0
BLADE: Filter Learning for General Purpose Computational Photography—0
Bit-depth color recovery via off-the-shelf super-resolution models—0
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified