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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2726–2750 of 3874 papers

TitleStatusHype
"Zero-Shot" Point Cloud UpsamplingCode0
Advancing biological super-resolution microscopy through deep learning: a brief review—0
Video Super-Resolution with Long-Term Self-Exemplars—0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh—0
Scene Text Telescope: Text-Focused Scene Image Super-ResolutionCode0
QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Light Field Super-Resolution With Zero-Shot Learning—0
Learning the Non-Differentiable Optimization for Blind Super-Resolution—0
Turning Frequency to Resolution: Video Super-Resolution via Event Cameras—0
Space-Time Distillation for Video Super-Resolution—0
MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network—0
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution—0
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Debiased Subjective Assessment of Real-World Image Enhancement—0
Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Perceptually-inspired super-resolution of compressed videos—0
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Feedback Pyramid Attention Networks for Single Image Super-Resolution—0
A self-adapting super-resolution structures framework for automatic design of GAN—0
Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified