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 2761–2770 of 3874 papers

TitleStatusHype
Self-Organized Residual Blocks for Image Super-Resolution—0
Cascaded Diffusion Models for High Fidelity Image Generation—0
Deep Hierarchical Super Resolution for Scientific Data—0
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding—0
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification—0
High-Frequency aware Perceptual Image Enhancement—0
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution—0
Unpaired Depth Super-Resolution in the WildCode0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
Combining Transformer Generators with Convolutional Discriminators—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified