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 576600 of 1589 papers

TitleStatusHype
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
Neural Nearest Neighbors NetworksCode0
Arbitrary Scale Super-Resolution for Brain MRI ImagesCode0
NCAP: Scene Text Image Super-Resolution with Non-CAtegorical PriorCode0
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-ResolutionCode0
MuS2: A Real-World Benchmark for Sentinel-2 Multi-Image Super-ResolutionCode0
Multi-scale deep neural networks for real image super-resolutionCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
DPSRGAN: Dilation Patch Super-Resolution Generative Adversarial NetworksCode0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
Camera Lens Super-ResolutionCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising ModelCode0
Multi-Modality Image Super-Resolution using Generative Adversarial NetworksCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-ResolutionCode0
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled DictionariesCode0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification LayersCode0
A Deep Journey into Super-resolution: A surveyCode0
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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