Image Super-Resolution
607 papers with code • 61 benchmarks • 39 datasets
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.
Benchmarks
These leaderboards are used to track progress in Image Super-Resolution
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Libraries
Use these libraries to find Image Super-Resolution models and implementations
ImageNet
KITTI
CelebA
FFHQ
CelebA-HQ
BSD
DIV2K
Urban100
VGGFace2
Set14
Subtasks
Latest papers
Partial Large Kernel CNNs for Efficient Super-Resolution
As a result, we introduce Partial Large Kernel CNNs for Efficient Super-Resolution (PLKSR), which achieves state-of-the-art performance on four datasets at a scale of $\times$4, with reductions of 68. 1\% in latency and 80. 2\% in maximum GPU memory occupancy compared to SRFormer-light.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
yuan-yutao/ecdp
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However, existing diffusion-based super-resolution methods have high time consumption with the use of iterative sampling, while the quality and consistency of generated images are less than ideal due to problems like color shifting.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution
Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neural networks, it has yet limited versatile applications due to the substantial computational costs.
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
jaehakim97/sr4ir
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Through extensive experiments, we demonstrate that our SR4IR achieves outstanding task performance by generating SR images useful for a specific image recognition task, including semantic segmentation, object detection, and image classification.
DRCT: Saving Image Super-resolution away from Information Bottleneck
ming053l/drct
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In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success.
Exploiting Self-Supervised Constraints in Image Super-Resolution
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing.
Ship in Sight: Diffusion Models for Ship-Image Super Resolution
In this context, our method explores in depth the problem of ship image super resolution, which is crucial for coastal and port surveillance.
CFAT: Unleashing TriangularWindows for Image Super-resolution
To overcome these weaknesses, we propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion and allows the model to access more unique sifting modes.
