Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective
Recently, deep learning (DL) approaches have become the main research frontier for
biological image reconstruction and enhancement problems thanks to their high …
biological image reconstruction and enhancement problems thanks to their high …
A review of deep learning methods for compressed sensing image reconstruction and its medical applications
Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …
paper, we review recent works using deep learning method to solve CS problem for images …
RARE: Image reconstruction using deep priors learned without groundtruth
Regularization by denoising (RED) is an image reconstruction framework that uses an
image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED …
image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED …
Physics-based learned design: optimized coded-illumination for quantitative phase imaging
Coded illumination can enable quantitative phase microscopy of transparent samples with
minimal hardware requirements. Intensity images are captured with different source …
minimal hardware requirements. Intensity images are captured with different source …
Dense recurrent neural networks for accelerated MRI: History-cognizant unrolling of optimization algorithms
SAH Hosseini, B Yaman, S Moeller… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge
about the forward encoding operator in a regularized reconstruction framework. Recently …
about the forward encoding operator in a regularized reconstruction framework. Recently …
Sgd-net: Efficient model-based deep learning with theoretical guarantees
Deep unfolding networks have recently gained popularity for solving imaging inverse
problems. However, the computational and memory complexity of data-consistency layers …
problems. However, the computational and memory complexity of data-consistency layers …
Solving phase retrieval with a learned reference
Fourier phase retrieval is a classical problem that deals with the recovery of an image from
the amplitude measurements of its Fourier coefficients. Conventional methods solve this …
the amplitude measurements of its Fourier coefficients. Conventional methods solve this …
Positive sparse signal denoising: What does a cnn learn?
Convolutional neural networks (CNNs) provide impressive empirical success in various
tasks; however, their inner workings generally lack interpretability. In this paper, we interpret …
tasks; however, their inner workings generally lack interpretability. In this paper, we interpret …
Learned patch-based regularization for inverse problems in imaging
Many modern approaches to image reconstruction are based on learning a regularizer that
implicitly encodes a prior over the space of images. For large-scale images common in …
implicitly encodes a prior over the space of images. For large-scale images common in …
Data-driven computational imaging for scientific discovery
In computational imaging, hardware for signal sampling and software for object
reconstruction are designed in tandem for improved capability. Examples of such systems …
reconstruction are designed in tandem for improved capability. Examples of such systems …