Application of Blind Deconvolution Based on the New Weighted L<sub>1</sub>-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images

Kim, Ji-Youn; Kim, Kyuseok; Lee, Youngjin

doi:10.1017/s143192762000183x

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…From the visual evaluation of the resulting image, it was confirmed that the sharpness of the deblurred image was improved compared with that of the degraded image in all areas of the tooth, PDL, and bone. Here, the deblurred image was generated by applying the previously l 0 -norm-based blind deconvolution method [49] to compare the results of the proposed method. Noise amplification in the deblurred image occurred significantly in all areas, and the noise level of the image could be reduced using the denoising method.…”

Section: Resultsmentioning

confidence: 99%

“…When using an objective function to obtain a deblurred image from a noisy image, it was difficult to give much weight to the regularization term due to increase the possibility of obtained the smoothing results. To overcome this limitation, our previous study demonstrated that proposed approach by setting the regularization term based on weight map and l 1 -norm improved the sharpness of image without cartoonish artifact [49]. The l 0 -normbased deconvolution method without weight function was applied to the deblurred image for improving the resolution.…”

Section: Proposed Restoration Scheme For Microscopic Imagesmentioning

confidence: 99%

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Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Kim,

Kim

2024

Applied Sciences

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Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Restoration Scheme For Microscopic Imagesmentioning

confidence: 99%

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Kim,

Kim

2024

Applied Sciences

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“…Optimization methods also find applications in microscopy, for example [129][130][131][132][133]. Their shortcomings are not so critical for this area, since the optical schemes correspond much more strictly to the concept of a linear spatially invariant system.…”

Section: Optimization-based Deconvolution Methodsmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

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“…Regularization [1,3] is to add structured risk to experience risk or experience loss. In this paper, the structured risk parameter is penalized by norm, which is used to limit the learning ability of the model and improve the generalization ability by preventing high deviation.…”

Section: Mathematical Models Formulationmentioning

confidence: 99%

“…For short-term load power forecasting, scholars have proposed a large number of power forecasting methods, including but not limited to neural network method, Markov chain method, support vector machine and so on [3]. Although the above algorithms have their own advantages and disadvantages, they are all based on massive data for target prediction and analysis.…”

Section: Introductionmentioning

confidence: 99%

Holiday Load Power Forecasting Based on SOIF-PAMAM method

2023

JEEE

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Fuzz testing is a widely used methodology for software testing. It collects feedback of each run and uses it for generation of interesting stimuli in the future. This paper discusses the ability and process of fuzz stimuli generator for hardware verification. We chose an asynchronous FIFO and a memory coherency verification using fuzz [1]. Our results substantiate the effectiveness of fuzz testing in the hardware verification process.

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Application of Blind Deconvolution Based on the New Weighted L₁-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images

Abstract: Abstract

Cited by 5 publications

References 34 publications

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Holiday Load Power Forecasting Based on SOIF-PAMAM method

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Application of Blind Deconvolution Based on the New Weighted L1-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images

Abstract: Abstract

Cited by 5 publications

References 34 publications

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Holiday Load Power Forecasting Based on SOIF-PAMAM method

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Application of Blind Deconvolution Based on the New Weighted L₁-norm Regularization with Alternating Direction Method of Multipliers in Light Microscopy Images