Super-Resolution
1270 papers with code • 0 benchmarks • 20 datasets
Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
( Credit: MemNet )
Benchmarks
These leaderboards are used to track progress in Super-Resolution
Datasets
MSU SR-QA Dataset
TextZoom
RealSRSet
DRealSR
Holopix50k
PROBA-V
Stanford Light Field
OST300
Subtasks
Latest papers
Partial Large Kernel CNNs for Efficient Super-Resolution
dslisleedh/PLKSR
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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$4): Methods and Results
zhengchen1999/ntire2024_imagesr_x4
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This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Deep learning-driven pulmonary arteries and veins segmentation reveals demography-associated pulmonary vasculature anatomy
Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions.
Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
By unfolding iterative optimization methods into neural networks, this approach offers enhanced interpretability and reliability compared to data-driven deep learning methods, and greater adaptability and generalization than model-based approaches.
Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution
Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction.
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
However, inaccurate alignment usually leads to aligned features with significant artifacts, which will be accumulated during propagation and thus affect video restoration.
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.
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
NJU-PCALab/AddSR
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Blind super-resolution methods based on stable diffusion showcase formidable generative capabilities in reconstructing clear high-resolution images with intricate details from low-resolution inputs.
