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18 lut 2021 · Abstract: Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods.
This paper provides a comprehensive survey on deep-learning-based super-resolution methods along with their applications and limitations. This paper categorizes the methods from an application perspective and a taxonomy is proposed.
We provide a detailed survey of deep learning-based SR, including the definition of the problem, dataset details, performance evaluation, deep learning methods used for SR, and specific applications where these SR methods were used and their performance.
12 sie 2024 · A deep learning roadmap is a structured guide designed to help individuals progress through the study of deep learning, from basic concepts to advanced applications. It serves as a comprehensive plan that outlines key areas of learning and development in deep learning.
Dive into Deep Learning. Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 500 universities from 70 countries. Star. Follow @D2L_ai. [Feb 2023] The book is forthcoming on Cambridge University Press (order).
18 sie 2021 · In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used.
22 wrz 2023 · We propose a fast acquisition and deep learning reconstruction framework to accelerate brain MRI at 0.055 tesla. The acquisition consists of a single average three-dimensional (3D) encoding with 2D partial Fourier sampling, reducing the scan time of T1- and T2-weighted imaging protocols to 2.5 and 3.2 minutes, respectively.
