Image Super-Resolution
618 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
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
zhengchen1999/ntire2024_imagesr_x4
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This paper reviews the NTIRE 2024 challenge on image super-resolution ($\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
aitical/sscsr
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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.
Efficient scene text image super-resolution with semantic guidance
Scene text image super-resolution has significantly improved the accuracy of scene text recognition.
VmambaIR: Visual State Space Model for Image Restoration
To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks.
Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
littlebeen/asddpm-adaptive-semantic-enhanced-ddpm
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However, the high-frequency details generated by DDPM often suffer from misalignment with HR images due to the model's tendency to overlook long-range semantic contexts.
