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 3001–3010 of 3874 papers

TitleStatusHype
Regularization by Denoising via Fixed-Point Projection (RED-PRO)—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation—0
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification—0
LatticeNet: Towards Lightweight Image Super-resolution with Lattice BlockCode0
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution—0
Transformation Consistency Regularization – A Semi-Supervised Paradigm for Image-to-Image Translation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified