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 301–350 of 1589 papers

TitleStatusHype
Single-sample image-fusion upsampling of fluorescence lifetime images—0
Partial Large Kernel CNNs for Efficient Super-ResolutionCode2
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-ResolutionCode2
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolutionCode1
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge ReportCode3
NTIRE 2024 Challenge on Image Super-Resolution (4): Methods and ResultsCode1
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution—0
Differentiable Search for Finding Optimal Quantization Strategy—0
LIPT: Latency-aware Image Processing TransformerCode1
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures—0
Efficient Learnable Collaborative Attention for Single Image Super-Resolution—0
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-ResolutionCode2
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
RefQSR: Reference-based Quantization for Image Super-Resolution Networks—0
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual LossCode2
DRCT: Saving Image Super-resolution away from Information BottleneckCode3
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Exploiting Self-Supervised Constraints in Image Super-ResolutionCode1
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network—0
Ship in Sight: Diffusion Models for Ship-Image Super ResolutionCode1
A Study in Dataset Pruning for Image Super-Resolution—0
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution—0
CFAT: Unleashing TriangularWindows for Image Super-resolutionCode2
Efficient scene text image super-resolution with semantic guidanceCode1
PAON: A New Neuron Model using Padé Approximants—0
CasSR: Activating Image Power for Real-World Image Super-Resolution—0
VmambaIR: Visual State Space Model for Image RestorationCode3
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-ResolutionCode1
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-ResolutionCode1
FeatUp: A Model-Agnostic Framework for Features at Any ResolutionCode5
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction—0
Activating Wider Areas in Image Super-ResolutionCode1
Learning Correction Errors via Frequency-Self Attention for Blind Image Super-Resolution—0
Efficient Diffusion Model for Image Restoration by Residual ShiftingCode5
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
XPSR: Cross-modal Priors for Diffusion-based Image Super-ResolutionCode2
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
Text-guided Explorable Image Super-resolution—0
Navigating Beyond Dropout: An Intriguing Solution Towards Generalizable Image Super Resolution—0
CAMixerSR: Only Details Need More "Attention"Code3
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of ArtifactsCode2
SeD: Semantic-Aware Discriminator for Image Super-ResolutionCode2
Simple Base Frame Guided Residual Network for RAW Burst Image Super-ResolutionCode1
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-ResolutionCode2
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Adaptive Convolutional Neural Network for Image Super-resolutionCode1
MambaIR: A Simple Baseline for Image Restoration with State-Space ModelCode5
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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
8SwinFIRPSNR29.36—Unverified
9CPAT+PSNR29.36—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