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 2451–2460 of 3874 papers

TitleStatusHype
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach—0
Robust Image Filtering Using Joint Static and Dynamic Guidance—0
Robust image reconstruction from multi-view measurements—0
Robust Multi-Image Based Blind Face Hallucination—0
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme—0
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography—0
Robust Regression via Deep Negative Correlation Learning—0
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior—0
Show:102550
← PrevPage 246 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified