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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 241–250 of 3874 papers

TitleStatusHype
DuCos: Duality Constrained Depth Super-Resolution via Foundation Model—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model—0
Rethinking Video Super-Resolution: Towards Diffusion-Based Methods without Motion Alignment—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution—0
Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications—0
DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-ResolutionCode2
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual LearningCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified