A Review of Image Denoising With Deep Learning

Yapici, Ahmet; Akçayol, M. Ali

doi:10.1109/iisec54230.2021.9672379

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…In the past decade, the development of deep learning has made amazing achievements, which has not only caused a research upsurge in academia, but has also been widely used in industry. In academia, deep learning has become a research hotspot in natural language processing [30], object detection [31,32], human activity recognition [33,34], and image denoising [35,36] and has achieved better results than traditional methods. In industry, deep learning has been successfully applied to product defect detection [37,38], image restoration [39], speech recognition [40] and other fields.…”

Section: Double Pool Attention Networkmentioning

confidence: 99%

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

You¹,

Zheng²,

Guo³

et al. 2023

Computers, Materials &Amp; Continua

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The authenticity identification of anti-counterfeiting codes based on mobile phone platforms is affected by lighting environment, photographing habits, camera resolution and other factors, resulting in poor collection quality of anti-counterfeiting codes and weak differentiation of anti-counterfeiting codes for high-quality counterfeits. Developing an anticounterfeiting code authentication algorithm based on mobile phones is of great commercial value. Although the existing algorithms developed based on special equipment can effectively identify forged anti-counterfeiting codes, the anti-counterfeiting code identification scheme based on mobile phones is still in its infancy. To address the small differences in texture features, low response speed and excessively large deep learning models used in mobile phone anti-counterfeiting and identification scenarios, we propose a feature-guided double pool attention network (FG-DPANet) to solve the reprinting forgery problem of printing anti-counterfeiting codes. To address the slight differences in texture features in high-quality reprinted anti-counterfeiting codes, we propose a feature guidance algorithm that creatively combines the texture features and the inherent noise feature of the scanner and printer introduced in the reprinting process to identify anti-counterfeiting code authenticity. The introduction of noise features effectively makes up for the small texture difference of high-quality anti-counterfeiting codes. The double pool attention network (DPANet) is a lightweight double pool attention residual network. Under the condition of ensuring detection accuracy, DPANet can simplify the network structure as much as possible, improve the network reasoning speed, and run better on mobile devices with low computing power. We conducted a series of experiments to evaluate the FG-DPANet proposed in this paper. Experimental results show that the proposed FG-DPANet can resist highquality and small-size anti-counterfeiting code reprint forgery. By comparing with the existing algorithm based on texture, it is shown that the proposed method has a higher authentication accuracy. Last but not least, the proposed scheme has been evaluated in the anti-counterfeiting code blurring scene, and the results show that our proposed method can well resist slight blurring of anti-counterfeiting images.

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Section: Double Pool Attention Networkmentioning

confidence: 99%

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

You¹,

Zheng²,

Guo³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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A Glance On Various Image Denoising Techniques

Ponnan,

Sreedivya,

Sreelatha

2023

2023 9th International Conference on Smart Computing and Communications (ICSCC)

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Comparison and Evaluation of TV-Based and Low-Rank-Based Destriping Algorithms for Hyperspectral Imagery

Shen,

Chen,

Cheng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing image data especially hyperspectral imagery (HSI) data has been widely applied in various research fields. However, hyperspectral imagery often suffers from significant noise contamination, such as stripe noise and Gaussian noise. These noises adversely affect the utilization of remote sensing data, prompting the development of numerous noise removal algorithms for hyperspectral imagery. Specifically, addressing stripe noise involves employing different strategies, including the employment of total-variation-based (TV-based) and low-rank-based algorithms. Yet, evaluating the effectiveness and applicability of these algorithms can be challenging due to variations in testing conditions provided by their respective authors during the proposal phase. Consequently, our aim was to offer a comprehensive and impartial evaluation of these stripe noise removal algorithms for hyperspectral imagery. Within the TV-based and low-rank-based algorithm categories, we had chosen ten distinct algorithms on which to conduct third-party testing and evaluation. We had considered various noise scenarios, resulting in a total of 48 noise configurations. Our evaluation encompassed multiple dimensions, including the quality of noise removal, computational speed, and parameter stability, thereby delivering a comprehensive assessment of the performance of different stripe noise removal algorithms. Additionally, we had conducted an analysis to identify the underlying causes of varying denoising results across different algorithms. This analysis offered valuable insights and recommendations for the future development of denoising algorithms.

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A Review of Image Denoising With Deep Learning

Cited by 3 publications

References 41 publications

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

Identification of a Printed Anti-Counterfeiting Code Based on Feature Guidance Double Pool Attention Networks

A Glance On Various Image Denoising Techniques

Comparison and Evaluation of TV-Based and Low-Rank-Based Destriping Algorithms for Hyperspectral Imagery

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