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 2001–2025 of 3874 papers

TitleStatusHype
DoA Estimation using MUSIC with Range/Doppler Multiplexing for MIMO-OFDM Radar—0
Do Deepfake Detectors Work in Reality?—0
Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation—0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Perceptual Image Super-Resolution with Progressive Adversarial Network—0
DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks—0
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
Double Sparse Multi-Frame Image Super Resolution—0
Double U-Net for Super-Resolution and Segmentation of Live Cell Images—0
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images—0
Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning—0
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors—0
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution—0
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks—0
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks—0
DSSR-Net for Super-Resolution Radar Range Profiles—0
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution—0
Dual-domain Modulation Network for Lightweight Image Super-Resolution—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss—0
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution—0
Dual Recovery Network with Online Compensation for Image Super-Resolution—0
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution—0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified