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 3591–3600 of 3874 papers

TitleStatusHype
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey—0
Co-learning Single-Step Diffusion Upsampler and Downsampler with Two Discriminators and Distillation—0
eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging—0
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos—0
Teacher-Student Network for Real-World Face Super-Resolution with Progressive Embedding of Edge Information—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction—0
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images—0
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified