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 751–800 of 1589 papers

TitleStatusHype
Semantic uncertainty intervals for disentangled latent spacesCode0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
Single image super-resolution based on trainable feature matching attention networkCode0
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception ModuleCode0
SinSR: Diffusion-Based Image Super-Resolution in a Single StepCode0
Spatially-Variant Degradation Model for Dataset-free Super-resolutionCode0
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral ImageryCode0
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SRCode0
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image SynthesisCode0
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal LearningCode0
Super-Resolution with Deep Convolutional Sufficient StatisticsCode0
Texture and Noise Dual Adaptation for Infrared Image Super-ResolutionCode0
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-ResolutionCode0
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation DecodersCode0
Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality AssessmentCode0
Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive FieldsCode0
Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity CluesCode0
To learn image super-resolution, use a GAN to learn how to do image degradation firstCode0
Toward Accurate Post-Training Quantization for Image Super ResolutionCode0
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image InformationCode0
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional NetworkCode0
Towards Progressive Multi-Frequency Representation for Image WarpingCode0
Trainable Loss Weights in Super-ResolutionCode0
Trained Model in Supervised Deep Learning is a Conditional Risk MinimizerCode0
Transfer Learning for Protein Structure Classification at Low ResolutionCode0
Trustworthy Image Super-Resolution via Generative PseudoinverseCode0
Ultra Sharp : Study of Single Image Super Resolution using Residual Dense NetworkCode0
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-ResolutionCode0
Universally Slimmable Networks and Improved Training TechniquesCode0
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-NetCode0
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksCode0
Volumetric Isosurface Rendering with Deep Learning-Based Super-ResolutionCode0
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-ResolutionCode0
Wide Activation for Efficient and Accurate Image Super-ResolutionCode0
"Zero-Shot" Super-Resolution using Deep Internal LearningCode0
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-ResolutionCode0
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution—0
Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution—0
A Frequency Domain Neural Network for Fast Image Super-resolution—0
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution—0
Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN—0
Infrared Image Super-Resolution via Lightweight Information Split Network—0
Infrared Image Super-Resolution via GAN—0
Facial Attribute Capsules for Noise Face Super Resolution—0
In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission—0
Interpretable Deep Multimodal Image Super-Resolution—0
Exploring the solution space of linear inverse problems with GAN latent geometry—0
Interpreting Super-Resolution Networks with Local Attribution Maps—0
Inter-Task Association Critic for Cross-Resolution Person Re-Identification—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