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 3226–3250 of 3874 papers

TitleStatusHype
Unsupervised Projection Networks for Generative Adversarial Networks—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
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
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
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Deformable Non-local Network for Video Super-ResolutionCode0
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
s-LWSR: Super Lightweight Super-Resolution NetworkCode0
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme—0
Unsupervised Learning for Real-World Super-Resolution—0
Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision—0
Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network—0
Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator—0
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise ModulationsCode0
Blind Super-Resolution Kernel Estimation using an Internal-GANCode0
Phase Retrieval using Untrained Neural Network Priors—0
Edge-Informed Single Image Super-ResolutionCode0
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors—0
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR VideoCode0
Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified