Implementation of Blind and Non-blind Deconvolution for Restoration of Defocused Image

Goilkar, Suhasini S.; Yadav, D. M.

doi:10.1109/esci50559.2021.9397046

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…Less deblurring errors are revealed by using the blind deconvolution approach to the blur ratio, which improves information visibility and leads to a lower squared error. These findings support hypothesis from [27].…”

Section: Deblurringsupporting

confidence: 90%

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Kristanto

Riadi

Prayudi

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Facial recognition is an essential aspect of conducting criminal action investigations. Captured images from the camera or the recording video can reveal the perpetrator's identity if their faces are deliberately or accidentally captured. However, many of these digital imagery results display the results of image quality that is not good when seen by the human eye. Hence, the facial recognition process becomes more complex and takes longer. This research aims to analyze face recognition on a low-quality image with noise, blur and brightness problem to help digital forensic investigator do an investigation in recognizing faces that the human eye can’t do. The Viola-Jones algorithm method has several processes such as the Haar feature, integral image, adaboost, and cascade classifier for detecting a face in an image. Detected face will be passed to the next process for recognition call Fisher’s Linear Discriminant (FLD), Local Binary Pattern’s (LBP) and Principal Component analysis (PCA). The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. In conclusion, from the analysis we determined that LBP approach is the best among the other recognition methods for blur and brightness problem, bet found PCA method is the best for recognize face in noise problem. The software's facial recognition feature shows one of the images in the database that the program suspects has the same face as the analyzed face image. The position of the face object in the image, whether or not there is an additional object that was not previously included in the image in the dataset, as well as the brightness level of an image and the color of the face's skin, all affect the accuracy rates.

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Section: Deblurringsupporting

confidence: 90%

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Kristanto

Riadi

Prayudi

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…For each of the techniques, the average MSE and SSIM values are computed, as presented in Table VI. As presented in Table VI, the assessment of deblurring reconstruction is performed by utilizing conventional deconvolution techniques such as the Lucy-Richardson, Wiener and blind deconvolution methods, as presented in [3], [64] and [65], respectively, alongside contemporary neural network-based approaches, namely Encoder-Decoder [66], ResNets [67] and a combination of Encoder-Decoder/ResNets configuration, as shown in this work, albeit without the inclusion of the L c term in the loss function (Eq. 6).…”

Section: F Performance Comparison With Different Deblurring Techniquesmentioning

confidence: 99%

Single Radar Image Motion Deblurring Through PSF Estimation and Convolution Regularizer

Sharma,

García-Fernández,

Álvarez-Narciandi

et al. 2024

Trans. Rad. Sys.

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This paper proposes a deep learning-based motion deblurring method for radar images using point-spread function (PSF) estimations. Motion blur is a common problem in radar imaging due to the movement of the radar platform or the target. The proposed method uses a convolutional neural network (CNN) to learn the mapping between the blurred and sharp images. In addition, the PSF of the motion blur is estimated using a separate CNN. The estimated point spread function (PSF) is utilized in conjunction with the input image to reconstruct a deblurred image. The reconstruction process is further optimized by incorporating the relationship between the input image, PSF, and the ground truth into the training loss term and optimizing it. The network is trained on a large dataset of simulated blurred and sharp radar images with corresponding PSFs. The developed method is evaluated on blurred radar images, subjected to varying degrees and lengths of blur, and compared with state-of-the art methods. The results show that the proposed method outperforms the existing methods both qualitatively and quantitatively. The proposed method can be used for various radar imaging applications, such as target detection and recognition, surveillance and navigation.

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“…Non-blind deconvolution [102][103][104] refers to the process of estimating a clear image from a blurred image using pre-measured PSF with the help of auxiliary methods.…”

Section: Non-blind Deconvolutionmentioning

confidence: 99%

End-to-End Algorithm Research in PACT—From Signal Processing to Reconstruction Solution to Image Processing

Liu,

Gao,

Cheng

et al. 2024

Preprint

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Photoacoustic computed tomography (PACT) is an innovative biomedical imaging technique that has gained significant application in the field of biomedicine due to its ability to visualize optical contrast with high resolution and deep tissue penetration. However, the inherent challenges associated with photoacoustic signal excitation, propagation and detection often result in suboptimal image quality. To overcome these limitations, researchers have developed various advanced algorithms that span the entire image reconstruction pipeline. This review paper aims to presents a detailed analysis of the latest advancements in PACT algorithms and synthesizes these algorithms into a coherent framework. We provide a tripartite analysis—from signal processing to reconstruction solution to image processing, covering a spectrum of techniques. The principles and methodologies, as well as their applicability and limitations, are thoroughly discussed. The primary objective of this study is to provide a thorough review of advanced algorithms applicable to PACT, offering both theoretical foundations and practical guidance for enhancing the imaging effect of PACT.

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Implementation of Blind and Non-blind Deconvolution for Restoration of Defocused Image

Cited by 5 publications

References 12 publications

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Forensic Analysis of Faces on Low-Quality Images using Detection and Recognition Methods

Single Radar Image Motion Deblurring Through PSF Estimation and Convolution Regularizer

End-to-End Algorithm Research in PACT—From Signal Processing to Reconstruction Solution to Image Processing

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