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 3176–3200 of 3874 papers

TitleStatusHype
Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal CorrelationsCode0
Fast Image Restoration With Multi-Bin Trainable Linear UnitsCode0
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution—0
Deep Blind Hyperspectral Image Fusion—0
Kernel Modeling Super-Resolution on Real Low-Resolution ImagesCode0
Deep learning at scale for subgrid modeling in turbulent flows—0
Super-resolution photoacoustic and ultrasound imaging with sparse arrays—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes—0
Frame and Feature-Context Video Super-Resolution—0
Learning to Have an Ear for Face Super-Resolution—0
Multi-grained Attention Networks for Single Image Super-Resolution—0
Lightweight Image Super-Resolution with Information Multi-distillation NetworkCode1
Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features—0
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery—0
Relative Pixel Prediction For Autoregressive Image Generation—0
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Deformable Non-local Network for Video Super-ResolutionCode0
s-LWSR: Super Lightweight Super-Resolution NetworkCode0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Unsupervised Learning for Real-World Super-Resolution—0
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified