A blind image deconvolution method based on noise variance estimation and blur type reorganization

Yi, Chong; Shimamura, Tetsuya

doi:10.1109/ispacs.2011.6146158

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Moreover, sister denoising techniques in medical images that estimate noise variance and restore the images include pristine work done by proposing a robust and novel blind imagery [25], a deconvolutional technique inherited from noise variance estimation; this model primarily performs noise variance estimation and eventually restores images by least-square filter method using the previously estimated parameters at each gradation. Advanced denoising and image smoothing approaches include techniques proposed by author Chong [26], which address the issue of super-resolution imageries in medical images by the implementation of a hybrid approach, combining the use of a thin plate reproducing kernel Hilbert space (RKHS) and Heaviside functions. This method function of image smoothing, i.e., the edge smoothing, is performed by Heaviside functions, while RKHS performs others.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

et al. 2022

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Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing.

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Section: Literature Reviewmentioning

confidence: 99%

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

et al. 2022

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Blur removal using blind deconvolution and gradient energy

Kanthan

Sujatha²

2016

2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

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An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Rajkumar

Malathi

2017

Bulletin EEI

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Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.

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A blind image deconvolution method based on noise variance estimation and blur type reorganization

Cited by 4 publications

References 19 publications

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

A Method Noise-Based Convolutional Neural Network Technique for CT Image Denoising

Blur removal using blind deconvolution and gradient energy

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

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