scholarly journals Метод обробки вiдеодaних з можливістю їх захисту після квaнтувaння

2021 ◽  
pp. 64-77
Author(s):  
Дмитро Сергійович Гаврилов ◽  
Сергій Степанович Бучік ◽  
Юрій Михайлович Бабенко ◽  
Сергій Сергійович Шульгін ◽  
Олександр Васильович Слободянюк
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Video Data ◽  
Video Image ◽  
Arithmetic Coding ◽  
Statistical Regularity ◽  
Signal To Noise ◽  
Level Of Confidence ◽  
Noise Ratio ◽  
Video Image Processing

The subject of research in the article is the video processing processes based on the JPEG platform for data transmission in the information and telecommunication network. The aim is to build a method for processing a video image with the possibility of protecting it at the quantization stage with subsequent arithmetic coding. That will allow, while preserving the structural and statistical regularity, to ensure the necessary level of accessibility, reliability, and confidentiality when transmitting video data. Task: research of known methods of selective video image processing with the subsequent formalization of the video image processing procedure at the quantization stage and statistical coding of significant blocks based on the JPEG platform. The methods used are an algorithm based on the JPEG platform, methods for selecting significant informative blocks, arithmetic coding. The following results were obtained. A method for processing a video image with the possibility of its protection at the stage of quantization with subsequent arithmetic coding has been developed. This method will allow, while preserving the structural and statistical regularity, to fulfill the set requirements for an accessible, reliable, and confidential transmission of video data. Ensuring the required level of availability is associated with a 30% reduction in the video image volume compared to the original volume. Simultaneously, the provision of the required level of confidence is confirmed by an estimate of the peak signal-to-noise ratio for an authorized user, which is dB. Ensuring the required level of confidentiality is confirmed by an estimate of the peak signal-to-noise ratio in case of unauthorized access, which is equal to dB. Conclusions. The scientific novelty of the results obtained is as follows: for the first time, two methods of processing video images at the quantization stage have been proposed. The proposed technologies fulfill the assigned tasks to ensure the required level of confidentiality at a given level of confidence. Simultaneously, the method of using encryption tables has a higher level of cryptographic stability than the method of using the key matrix. It is due to a a more complex mathematical apparatus. Which, in turn, increases the time for processing the tributes. To fulfill the requirement of data availability, it is proposed to use arithmetic coding for info-normative blocks, which should be more efficient compared with the methods of code tables. So, the method of using the scoring tables has greater cryptographic stability, and the method of using the matrix-key has higher performance. Simultaneously, the use of arithmetic coding will satisfy the need for accessibility by reducing the initial volume.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

Measurement of signal-to-noise ratio in dysphonic voices by image processing of spectrograms

2014 ◽  
Vol 61-62 ◽  
pp. 17-32 ◽  
Author(s):  
Maurílio N. Vieira ◽  
João Pedro H. Sansão ◽  
Hani C. Yehia
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

High-signal-to-noise-ratio image processing in low-signal-to-bias-ratio environments

1997 ◽  
Author(s):  
Hakan Urey ◽  
William T. Rhodes ◽  
H. John Caulfield ◽  
Zafer Urey
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Ratio Image ◽  
Bias Ratio ◽  
Noise Ratio

scholarly journals Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

2012 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
PZ Nadila ◽  
YHP Manurung ◽  
SA Halim ◽  
SK Abas ◽  
G Tham ◽  
...  
Keyword(s):  
Image Processing ◽  
User Interface ◽  
Image Quality ◽  
Digital Radiography ◽  
Graphical User Interface ◽  
Signal To Noise Ratio ◽  
Quality Measurement ◽  
Noise Removal ◽  
Signal To Noise ◽  
Noise Ratio

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB. 

scholarly journals Agro Image De-Noising (Aid) for Enhanced Agricultural Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2474-2478
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Photographic Images ◽  
Spline Wavelets ◽  
Soft Threshold ◽  
Noise Ratio ◽  
Processing Techniques

Several Noises may be present in acquired images. This is an undesired feature for image processing techniques that analyze these images. Image de-noising helps improve efficiency of image processing. Many image de-noising methods have been proposed and exist in literature. Image de-noising methods for agricultural images have been proposed to a lesser extent when compared to the bright medical or photographic images. This paper proposes Agricultural Image De-noising (AID) which uses a discrete wavelet transform (DWT) to eliminate noise in agricultural images. This study uses specific kind of wavelet family spline wavelet transforms with appropriate decomposition level and the wavelet coefficients are analysed with hard and soft threshold methods. The denoised image using various spline wavelets is compared of hard threshold and soft threshold are assessed. The performance of AID is calculated using the peak signal to noise ratio (PSNR) and signal to noise ratio (SNR).

scholarly journals Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

2021 ◽  
Vol 11 (1) ◽  
pp. 399-410
Author(s):  
Kaitheri Thacharedath Dilna ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Image Processing ◽  
Signal To Noise Ratio ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Signal To Noise ◽  
Statistical Measures ◽  
Noise Ratio ◽  
Scanned Images

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

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