Single-image deblurring with neural networks: A comparative survey

Koh, Jaihyun; Lee, Jangho; Yoon, Sungroh

doi:10.1016/j.cviu.2020.103134

Cited by 69 publications

(31 citation statements)

References 62 publications

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“…To further improve the technology, we plan to test machine learning approaches [87][88][89] to reduce these identification artifacts for relatively small ensembles. Another way to optimize (reduce) the number of the ensemble elements is to evaluate the most informative ensemble members (as it was done in [42] by choosing the elements with the largest projection on the initial discrepancy) or aggregate the ensemble members (as it was done in [90], where the elements were aggregated according to the left singular vectors of the sensitivity operator).…”

Section: Discussionmentioning

confidence: 99%

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Penenko

Tsvetova

et al. 2021

Atmosphere

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Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

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Section: Discussionmentioning

confidence: 99%

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Penenko

Tsvetova

et al. 2021

Atmosphere

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“…Unsurprisingly, many learning-based deblurring methods have been raised in recent years. A very recent review [23] illustrates how learning-based deblurring methods are developed (See Fig. 3), and evaluates the performance of different approaches.…”

Section: Previous Reviewsmentioning

confidence: 99%

“…After Nah et al[38] is proposed, direct image-to-image regression methods (marked in blue) are developed. Source: Koh et al[23]…”

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confidence: 99%

Blind Image Deblurring: a Review

Xue¹

2022

Preprint

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In this paper, we first formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind deblurring, and introduce some typical optimization-based methods and learning-based methods. Finally, we review the highlights of the theoretical analysis part of Levin et al. [32], which could answer some very fundamental questions about blind deblurring.

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“…Recently, a number of deep learning-based prefiltering approaches have been adopted for targeted coding optimization. These include denoising [29], [30], motion deblurring [31], [32], contrast enhancement [33], edge detection [34], [35], and so on. Another important topic is closely related to the analysis of video content semantics, for example, object instance, saliency attention, and texture distribution, and its application to intelligent video coding.…”

Section: O V E R V I E W O F D N N -B a S E D V I D E O P R E P Rmentioning

confidence: 99%

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Ding

Chen

et al. 2021

Proc. IEEE

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Significant advances in video compression systems have been made in the past several decades to satisfy the near-exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks, including preprocessing, coding, and postprocessing, which have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI)-powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review recent technical advances in video compression systems extensively, with an emphasis on deep neural network (DNN)based approaches, and then present three comprehensive case studies. On preprocessing, we show a switchable texturebased video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement Manuscript

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Single-image deblurring with neural networks: A comparative survey

Cited by 69 publications

References 62 publications

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Blind Image Deblurring: a Review

Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

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