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 1811–1820 of 3874 papers

TitleStatusHype
CoDe: An Explicit Content Decoupling Framework for Image Restoration—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
UGPNet: Universal Generative Prior for Image Restoration—0
Compressing Deep Image Super-resolution Models—0
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features—0
Single particle algorithms to reveal cellular nanodomain organization—0
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer—0
A Survey on Super Resolution for video Enhancement Using GAN—0
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified