An Improved Maximum-Likelihood Estimation Algorithm for Blind Image Deconvolution Based on Noise Variance Estimation

Yi, Chong; Shimamura, Tetsuya

doi:10.2299/jsp.16.629

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Statistical methods are based on various methods for estimating parameters from mathematical statistics. These include methods based on a maximum likelihood estimates or an estimate of the posterior maximum [32][33][34]. Many deconvolution algorithms are either entirely based on statistical methods or use many of their elements to one degree or another.…”

Section: Deconvolution Methods Classificationmentioning

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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Section: Deconvolution Methods Classificationmentioning

confidence: 99%

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

Makarkin

Bratashov

2021

Micromachines

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“…On the other hand, maximum likelihood-based image deblurring algorithms do not require the PSF information, hence, they are an effective tool for blind image deblurring [59]. Yi and Shimamura [60] developed an improved maximum likelihood-based blind image restoration technique for degraded images affected by noise and blur. Moreover, in blind-image restoration techniques, unsharp masking is a classic technique to restore the blurry and noisy images and subsequently enhance the details and improve the edge-information [61].…”

Section: Filter Algorithmsmentioning

confidence: 99%

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Modak,

Heil,

Stein

2024

Remote Sensing

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

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Image deblurring to improve the grain monitoring in a rice combine harvester

Rahimi-Ajdadi

Mollazade

2023

Smart Agricultural Technology

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An Improved Maximum-Likelihood Estimation Algorithm for Blind Image Deconvolution Based on Noise Variance Estimation

Cited by 4 publications

References 13 publications

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

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

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Image deblurring to improve the grain monitoring in a rice combine harvester

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