Image Super-Resolution
610 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
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.
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
BlindDiff seamlessly integrates the MAP-based optimization into DMs, which constructs a joint distribution of the low-resolution (LR) observation, high-resolution (HR) data, and degradation kernels for the data and kernel priors, and solves the blind SR problem by unfolding MAP approach along with the reverse process.
Activating Wider Areas in Image Super-Resolution
arsenalcheng/mma
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The prevalence of convolution neural networks (CNNs) and vision transformers (ViTs) has markedly revolutionized the area of single-image super-resolution (SISR).
Efficient Diffusion Model for Image Restoration by Residual Shifting
zsyoaoa/resshift
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While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps.
SeD: Semantic-Aware Discriminator for Image Super-Resolution
lbc12345/sed
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In particular, one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner.
