Increasing the resolution of a non-negative brightness image distorted by a linear transformation

Chulichkov, A. I.; Molkov, Egor M.

doi:10.1109/itnt49337.2020.9253301

Cited by 2 publications

(2 citation statements)

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“…One way of dealing with the above difficulties is based on the following idea [ 8 , 27 ]. Let the distortion introduced by a linear image registration system be such that the image of a point object is a spot.…”

Section: Methodsmentioning

confidence: 99%

Measurement Reduction Methods for Processing Tomographic Images

Chulichkov¹,

Балакин²

2023

Sensors

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The importance of development of new methods for reconstruction of an object image given its sinogram and some additional information about the object stems from the possibility of artifact presence in the reconstructed image, or its insufficient sharpness when the used additional information does not hold. The problem of recovering artifact-free images of the studied object from tomography data is considered in the framework of the theory of computer-aided measuring systems. Methods for solving it are developed. They are based on narrowing the class of possible images using less artifact-inducing information. An example of such information is the natural condition of non-negativeness of the estimated brightnesses. The main problem that arises is the large dimensionality of the images, which prevents the use of direct algorithms. One proposed method is based on local approach, namely correction of the result of unfiltered backprojection by applying a locally (in the space of the output image) optimal linear transformation. Another method processes a sinogram directly, without using backprojection, using iterative implementation of the measurement reduction technique. Examples of use of the proposed methods for processing teeth sinograms are given.

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Section: Methodsmentioning

confidence: 99%

Measurement Reduction Methods for Processing Tomographic Images

Chulichkov¹,

Балакин²

2023

Sensors

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“…Capturing facial images can be done through CCTV cameras, smartphones, and other equipment to capture an object into a digital signal. However, there are certain issues with the quality of digital images, such as pixel quality from the camera [2], image blur [3], light intensity that is too little or too much [4], [5], and noise [6]. Poor image quality can result from a number of factors, including the camera's capacity to catch light, its undesirable location, and the amount of or absence of light that illuminates the subject.…”

Section: Introductionmentioning

confidence: 99%

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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Increasing the resolution of a non-negative brightness image distorted by a linear transformation

Cited by 2 publications

References 5 publications

Measurement Reduction Methods for Processing Tomographic Images

Measurement Reduction Methods for Processing Tomographic Images

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

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