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 2021–2030 of 3874 papers

TitleStatusHype
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
DuCos: Duality Constrained Depth Super-Resolution via Foundation Model—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation—0
Show:102550
← PrevPage 203 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified