Deep learning for single image super-resolution: A brief review
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …
Plug-and-play image restoration with deep denoiser prior
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly
serve as the image prior for model-based methods to solve many inverse problems. Such a …
serve as the image prior for model-based methods to solve many inverse problems. Such a …
Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …
computational imaging problems through the integration of physical models and learned …
Deep equilibrium architectures for inverse problems in imaging
Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …
architectures inspired by a fixed number of iterations of an optimization method. The number …
Deep blind super-resolution for satellite video
Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …
Deep internal learning: Deep learning from a single input
Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …
Yet, in many cases, there is value in training a network just from the input at hand. This is …
Image restoration by denoising diffusion models with iteratively preconditioned guidance
Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …
restoration problems. An alternative for training a" task-specific" network for each …
Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers
The single image super-resolution task is one of the most examined inverse problems in the
past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior …
past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior …
Image-adaptive GAN based reconstruction
In the recent years, there has been a significant improvement in the quality of samples
produced by (deep) generative models such as variational auto-encoders and generative …
produced by (deep) generative models such as variational auto-encoders and generative …
Generative adversarial network for desert seismic data denoising
H Wang, Y Li, X Dong - IEEE Transactions on Geoscience and …, 2020 - ieeexplore.ieee.org
Seismic exploration is a kind of exploration method for oil and gas resources. However, the
disturbance of numerous random noise will decrease the quality and signal-to-noise ratio …
disturbance of numerous random noise will decrease the quality and signal-to-noise ratio …