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 3526–3550 of 3874 papers

TitleStatusHype
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks—0
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale WarpingCode0
Gated Fusion Network for Joint Image Deblurring and Super-ResolutionCode0
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
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
CT-image Super Resolution Using 3D Convolutional Neural Network—0
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?—0
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution—0
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN—0
Non-Local Recurrent Network for Image RestorationCode0
Super-Resolution using Convolutional Neural Networks without Any Checkerboard ArtifactsCode0
Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network—0
Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images—0
Scale-Transferrable Object Detection—0
Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior—0
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion CompensationCode0
A Papier-Mâché Approach to Learning 3D Surface Generation—0
Mesoscopic Facial Geometry Inference Using Deep Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified