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

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Showing 3861–3870 of 3874 papers

TitleStatusHype
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language ModelCode0
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
Deep Iterative Residual Convolutional Network for Single Image Super-ResolutionCode0
Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-ResolutionCode0
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid NetworksCode0
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in NetworkCode0
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual NetworkCode0
UGoDIT: Unsupervised Group Deep Image Prior Via Transferable WeightsCode0
Handheld Multi-Frame Super-ResolutionCode0
The Perception-Distortion TradeoffCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified