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 211–220 of 3874 papers

TitleStatusHype
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
Accelerating the Super-Resolution Convolutional Neural NetworkCode1
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable AlignmentCode1
Burst Image Restoration and EnhancementCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Show:102550
← PrevPage 22 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified