DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network

Agarwal, Chirag; Khobahi, Shahin; Bose, Arindam; Soltanalian, Mojtaba; Schonfeld, Dan

doi:10.1109/icip40778.2020.9190825

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…Deconvolution methods require an understanding of their applicability to a practical condition, as well as optimization of computation cost and accuracy (for features of various methods see Naik & Sahu 2013). The RL method is well studied and has been used to incorporate regularization (e.g., van Kempen & van Vliet 2000;Dey et al 2006;Yuan et al 2008;Yongpan et al 2010) and the recent trend of deep learning (e.g., Agarwal et al 2020). For Chandra users, RL deconvolution with a single PSF is frequently used because the method is already implemented as arestore in the Chandra Interactive Analysis of Observations (CIAO; Fruscione et al 2006), Chandraʼs standard data processing package.…”

Section: Comparison To Other Methodsmentioning

confidence: 99%

Richardson–Lucy Deconvolution with a Spatially Variant Point-spread Function of Chandra: Supernova Remnant Cassiopeia A as an Example

Sakai

Yamada

Sato

et al. 2023

ApJ

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Richardson–Lucy (RL) deconvolution is one of the classical methods widely used in X-ray astronomy and other areas. Amid recent progress in image processing, RL deconvolution still leaves much room for improvement under realistic situations. One direction is to include the positional dependence of a point-spread function (PSF), so-called RL deconvolution with a spatially variant PSF (RLsv). Another is the method of estimating a reliable number of iterations and their associated uncertainties. We developed a practical method that incorporates the RLsv algorithm and the estimation of uncertainties. As a typical example of bright and high-resolution images, the Chandra X-ray image of the supernova remnant Cassiopeia A was used in this paper. RLsv deconvolution enables us to uncover the smeared features in the forward/backward shocks and jet-like structures. We constructed a method to predict the appropriate number of iterations using statistical fluctuation of the observed images. Furthermore, the uncertainties were estimated by error propagation from the last iteration, which was phenomenologically tested with the observed data. Thus, our method is a practically efficient framework to evaluate the time evolution of the remnants and their fine structures embedded in high-resolution X-ray images.

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Section: Comparison To Other Methodsmentioning

confidence: 99%

Richardson–Lucy Deconvolution with a Spatially Variant Point-spread Function of Chandra: Supernova Remnant Cassiopeia A as an Example

Sakai

Yamada

Sato

et al. 2023

ApJ

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show abstract

“…To solve the corresponding ill-posed inverse problem, some studies have resorted to variational methods that incorporate prior information on the unknown clean image, such as promoting sparsity [22], or some prior learned from data [1]- [4]. The other strategy combines the advantages of deep neural networks and variational approaches by linking each layer of a deep network to one iteration of the baseline iterative algorithm, and learning the algorithm hyperparameters from data by using deep unfolding methods [5], [6], [23]. Although the aforementioned methods produce very good results, they are typically limited to simplistic scenarios, which are spatially invariant blur kernels.…”

Section: A Deblurring In the Presence Of Noisementioning

confidence: 99%

“…However, a small exposure time causes noise. Numerous methods have been proposed to address the deblurring task, ranging from spatially invariant [1]- [6] to spatially variant blur [7]- [12]. Meanwhile, many approaches have been proposed for denoising with remarkable results [13]- [16].…”

Section: Introductionmentioning

confidence: 99%

Concurrent Video Denoising and Deblurring for Dynamic Scenes

Katsaros

Ostrowski²,

Węsierski³

et al. 2021

IEEE Access

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“…Although the data-driven approaches can handle large and complex datasets, they are ignorant to the underlying problem-level reasoning that may be available. Therefore, it is vital to develop a hybrid data-driven and domain-knowledge-aware framework to enhance the accuracy and efficiency of deep learning-based COVID-19 diagnosis using CT/X-ray images, while reducing the computational cost as much as possible (we refer an interested reader to consult [4, 5, 6, 7] and the references therein for a detailed explanation of the existing model-based deep learning models).…”

Section: Introductionmentioning

confidence: 99%

CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images

Khobahi

Agarwal

Soltanalian

2020

Preprint

Self Cite

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In late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 , emerged in Wuhan city, Hubei, China, and resulted in a global pandemic-claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a timeconsuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-theart methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community 1 .

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DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network

Cited by 12 publications

References 22 publications

Richardson–Lucy Deconvolution with a Spatially Variant Point-spread Function of Chandra: Supernova Remnant Cassiopeia A as an Example

Richardson–Lucy Deconvolution with a Spatially Variant Point-spread Function of Chandra: Supernova Remnant Cassiopeia A as an Example

Concurrent Video Denoising and Deblurring for Dynamic Scenes

CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images

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