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 1281–1290 of 3874 papers

TitleStatusHype
Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow ModelsCode0
A Fusion-Guided Inception Network for Hyperspectral Image Super-ResolutionCode0
Lightweight Feature Fusion Network for Single Image Super-ResolutionCode0
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-ResolutionCode0
Lightweight Image Super-Resolution with Adaptive Weighted Learning NetworkCode0
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-ResolversCode0
A Cone-Beam X-Ray CT Data Collection designed for Machine LearningCode0
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive NetworkCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
A Fully Progressive Approach to Single-Image Super-ResolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified