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

Showing 226250 of 1589 papers

TitleStatusHype
A Systematic Survey of Deep Learning-based Single-Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
Fully 11 Convolutional Network for Lightweight Image Super-ResolutionCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-ResolutionCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
Generative Adversarial Super-Resolution at the Edge with Knowledge DistillationCode1
Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI ScansCode1
ControlSR: Taming Diffusion Models for Consistent Real-World Image Super ResolutionCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
GRFormer: Grouped Residual Self-Attention for Lightweight Single Image Super-ResolutionCode1
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-ResolutionCode1
A Spectral Diffusion Prior for Hyperspectral Image Super-ResolutionCode1
2DQuant: Low-bit Post-Training Quantization for Image Super-ResolutionCode1
Collapsible Linear Blocks for Super-Efficient Super ResolutionCode1
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-ResolutionCode1
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time seriesCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Dual Super-Resolution Learning for Semantic SegmentationCode1
Hierarchical Residual Attention Network for Single Image Super-ResolutionCode1
HiFaceGAN: Face Renovation via Collaborative Suppression and ReplenishmentCode1
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-ResolutionCode1
Conditional Hyper-Network for Blind Super-Resolution with Multiple DegradationsCode1
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient ConvolutionCode1
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Benchmark Results

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