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 3511–3520 of 3874 papers

TitleStatusHype
A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CTCode0
Decouple Learning for Parameterized Image OperatorsCode0
Perceptual Video Super Resolution with Enhanced Temporal Consistency—0
An Attention-Based Approach for Single Image Super Resolution—0
Optimal Physical Preprocessing for Example-Based Super-Resolution—0
Image Super-Resolution Using Very Deep Residual Channel Attention NetworksCode2
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression—0
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis—0
Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face HallucinationCode0
Multi-modal Image Processing based on Coupled Dictionary Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified