Image restoration via improved Wiener filter applied to optical sparse aperture systems

Chen, Hao; Cen, Zhaofeng; Wang, Chuanchuan; Lan, Shun; Li, Xiaotong

doi:10.1016/j.ijleo.2017.08.102

Cited by 21 publications

(4 citation statements)

References 10 publications

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“…The purpose of the proposed method is to find the optimal PSF considering the human visible system, and then the best results can be achieved by applying a contextualized image restoration algorithm. Figure 14 shows the experimental results of the iterative restoration method using a regularization term, a restoration method based on the Wiener filter, and the Lucy-Richardson-Rosen method [29,30,63]. Here, the results above use detector 2 and another result below uses detector 3.…”

Section: Methodsmentioning

confidence: 99%

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Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging

Cha,

Lee,

Kim

2023

Sensors

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An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient’s dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system.

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Section: Methodsmentioning

confidence: 99%

“…Traditionally, Richardson-Lucy and Wiener filters are classic deconvolution methods [29,30], and fast deconvolution is performed to obtain restored images with improved sharpness. However, these images suffer from ringing artifacts [27].…”

Section: Introductionmentioning

confidence: 99%

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging

Cha,

Lee,

Kim

2023

Sensors

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“…Step 2: Further compute PSNR [ 31 ] along with SNR as in Eq (1) , which is deployed to estimate the quality of the image. where, , H Mean indicates harmonic mean, P and Q matrix dimension of Img , E 0 ( i , j ) indicates the pixels’ gray value in j th the column and i th row of the original image and E ′( i , j ) indicates the pixels’ gray value in j th the column and i th row of the restored image.…”

Section: An Overview Of M-segnet-based Lung Disease Segmentation and ...mentioning

confidence: 99%

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Shafi,

Chinnappan

2024

PLoS ONE

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Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.

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“…Some of the filtering techniques include Wiener filtering and wave atom transform used in degraded images and those affected by noise, blur, etc. [4,23].…”

Section: Introductionmentioning

confidence: 99%

Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

Basha

Venkateswarlu

2019

Journal of Intelligent Systems

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Abstract The image restoration (IR) technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine (LR-SVM). This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The extracted block-by-block values of images are used to enhance the classification process of IR. In training, the imperfections on the image are easily identified by setting the target vectors as the original images. Then, the noisy image is given at LR-SVM testing, based on the original image restored from the dictionary. Finally, the image block from the testing stage is enhanced using the hybrid Laplacian of Gaussian (HLOG) filter. The denoising of the HLOG filter provides enhanced results by using block-by-block values. This proposed approach is named as LR-SVM-HLOG. A dataset used in this LR-SVM-HLOG method is the Berkeley Segmentation Database. The performance of LR-SVM-HLOG was analyzed as peak signal-to-noise ratio (PSNR) and structural similarity index. The PSNR values of the house and pepper image (color image) are 40.82 and 36.56 dB, respectively, which are higher compared to the inter- and intra-block sparse estimation method and block matching and three-dimensional filtering for color images at 20% noise.

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Image restoration via improved Wiener filter applied to optical sparse aperture systems

Cited by 21 publications

References 10 publications

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging

Development of Adaptive Point-Spread Function Estimation Method in Various Scintillation Detector Thickness for X-ray Imaging

Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration

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