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 551575 of 1589 papers

TitleStatusHype
Operational Neural Networks for Parameter-Efficient Hyperspectral Single-Image Super-ResolutionCode0
Efficient Residual Dense Block Search for Image Super-ResolutionCode0
Combination of Single and Multi-frame Image Super-resolution: An Analytical PerspectiveCode0
Efficient Deep Neural Network for Photo-realistic Image Super-ResolutionCode0
Efficient Integer-Arithmetic-Only Convolutional Neural NetworksCode0
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid NetworkCode0
CISRNet: Compressed Image Super-Resolution NetworkCode0
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging SystemsCode0
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-ResolutionCode0
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected NetworkCode0
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinementCode0
Non-Local Recurrent Network for Image RestorationCode0
Neural Nearest Neighbors NetworksCode0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
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
Edge-Informed Single Image Super-ResolutionCode0
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-ResolutionCode0
E2FIF: Push the limit of Binarized Deep Imagery Super-resolution using End-to-end Full-precision Information FlowCode0
Multi-scale deep neural networks for real image super-resolutionCode0
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
DWA: Differential Wavelet Amplifier for Image Super-ResolutionCode0
Multimodal Sensor Fusion In Single Thermal image Super-ResolutionCode0
Multi-scale Residual Network for Image Super-ResolutionCode0
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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