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 2001–2050 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
DuCos: Duality Constrained Depth Super-Resolution via Foundation Model—0
Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations—0
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
Dynamic Snake Upsampling Operater and Boundary-Skeleton Weighted Loss for Tubular Structure Segmentation—0
Dynamic super-resolution in particle tracking problems—0
DynVideo-E: Harnessing Dynamic NeRF for Large-Scale Motion- and View-Change Human-Centric Video Editing—0
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery—0
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution—0
SCALES: Boost Binary Neural Network for Image Super-Resolution with Efficient Scalings—0
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference—0
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression—0
Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images—0
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning—0
Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection—0
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention—0
E-FCNN for tiny facial expression recognition—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Effective Invertible Arbitrary Image Rescaling—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
Effect of structure-based training on 3D localization precision and quality—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified