SOTAVerified

Image Super-Resolution

Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.

Papers

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Showing 1251–1300 of 1589 papers

TitleStatusHype
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration—0
Training a Task-Specific Image Reconstruction Loss—0
Training Set Effect on Super Resolution for Automated Target Recognition—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function—0
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images—0
Transformers in Vision: A Survey—0
Transport-based analysis, modeling, and learning from signal and data distributions—0
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution—0
Triple Attention Mixed Link Network for Single Image Super Resolution—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—0
TSP-Mamba: The Travelling Salesman Problem Meets Mamba for Image Super-resolution and Beyond—0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks—0
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution—0
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt—0
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance—0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
Uncertainty Estimation for Super-Resolution using ESRGAN—0
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model—0
Understanding Opportunities for Efficiency in Single-image Super Resolution Networks—0
Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution—0
Underwater Image Super-Resolution using Generative Adversarial Network-based Model—0
Unified Dynamic Convolutional Network for Super-Resolution with Variational Degradations—0
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution—0
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration—0
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution—0
An Optimal Transport Perspective on Unpaired Image Super-Resolution—0
Unpaired MRI Super Resolution with Contrastive Learning—0
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN—0
Unsupervised Degradation Learning for Single Image Super-Resolution—0
Unsupervised Image Noise Modeling with Self-Consistent GAN—0
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns—0
Unsupervised Image Super-Resolution with an Indirect Supervised Path—0
Unsupervised Learning for Real-World Super-Resolution—0
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors—0
Unsupervised Projection Networks for Generative Adversarial Networks—0
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution—0
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution—0
USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution—0
VarSR: Variational Super-Resolution Network for Very Low Resolution Images—0
Video Super-Resolution Using a Grouped Residual in Residual Network—0
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution—0
Wavelet-SRNet: A Wavelet-Based CNN for Multi-Scale Face Super Resolution—0
WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution—0
Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks—0
Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model—0
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey—0
Wider Channel Attention Network for Remote Sensing Image Super-resolution—0
XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation—0
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All datasetsSet14 - 4x upscalingBSD100 - 4x upscalingUrban100 - 4x upscalingManga109 - 4x upscalingSet5 - 2x upscalingSet14 - 2x upscalingSet5 - 3x upscalingBSD100 - 2x upscalingUrban100 - 2x upscalingSet14 - 3x upscalingUrban100 - 3x upscalingBSD100 - 3x upscaling

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR29.54—Unverified
2HMA†PSNR29.51—Unverified
3Hi-IR-LPSNR29.49—Unverified
4HAT-LPSNR29.47—Unverified
5HAT_FIRPSNR29.44—Unverified
6DRCTPSNR29.4—Unverified
7HATPSNR29.38—Unverified
8CPAT+PSNR29.36—Unverified
9SwinFIRPSNR29.36—Unverified
10CPATPSNR29.34—Unverified
#ModelMetricClaimedVerifiedStatus
1DRCT-LPSNR28.16—Unverified
2HMA†PSNR28.13—Unverified
3Hi-IR-LPSNR28.13—Unverified
4HAT-LPSNR28.09—Unverified
5HAT_FIRPSNR28.07—Unverified
6CPAT+PSNR28.06—Unverified
7DRCTPSNR28.06—Unverified
8HATPSNR28.05—Unverified
9CPATPSNR28.04—Unverified
10SwinFIRPSNR28.03—Unverified
#ModelMetricClaimedVerifiedStatus
1Hi-IR-LPSNR28.72—Unverified
2DRCT-LPSNR28.7—Unverified
3HMA†PSNR28.69—Unverified
4HAT-LPSNR28.6—Unverified
5HAT_FIRPSNR28.43—Unverified
6DRCTPSNR28.4—Unverified
7HATPSNR28.37—Unverified
8CPAT+PSNR28.33—Unverified
9CPATPSNR28.22—Unverified
10PFTPSNR28.2—Unverified