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 2481–2490 of 3874 papers

TitleStatusHype
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification—0
High-Frequency aware Perceptual Image Enhancement—0
Unpaired Depth Super-Resolution in the WildCode0
Towards Compact Single Image Super-Resolution via Contrastive Self-distillationCode1
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution—0
MIASSR: An Approach for Medical Image Arbitrary Scale Super-ResolutionCode1
Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile DevicesCode1
Combining Transformer Generators with Convolutional Discriminators—0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and BeyondCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified