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 2291–2300 of 3874 papers

TitleStatusHype
Mitigating Channel-wise Noise for Single Image Super Resolution—0
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery—0
A comprehensive review on Plant Leaf Disease detection using Deep learning—0
A Comprehensive Review of Deep Learning-based Single Image Super-resolution—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks—0
Translation-based Video-to-Video Synthesis—0
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting From Class-Specific Image Priors—0
Content-Aware Local GAN for Photo-Realistic Super-Resolution—0
MNSRNet: Multimodal Transformer Network for 3D Surface Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified