Yazar "Gungor, Murat Alparslan" seçeneğine göre listele

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    A comparative study on wavelet denoising for high noisy CT images of COVID-19 disease
    (Elsevier Gmbh, 2021) Gungor, Murat Alparslan
    Coronavirus disease (COVID-19), detected in Wuhan City, Hubei Province, China, is a pandemic disease and affecting all people in the world. Real-time reverse transcription polymerase chain reaction (RT-PCR) test is the standard clinical tool for the diagnosis of COVID-19. Computed Tomography (CT) is an alternative method to RT-PCR test for the diagnosis of COVID-19 due to some disadvantages of the RT-PCR test. In this method, the target is to determine coronavirus pneumonia from CT images. However, high noise decreases the image quality, so a noise reduction filter is used. The wavelet functions are widely used to reduce noise in images. In this study, a performance comparison of the different wavelet functions in CT image denoising is proposed. Significant remarks are obtained from the analysis to improve the quality for CT exams of COVID-19 disease.
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    DEVELOPING A COMPRESSION PROCEDURE BASED ON THE WAVELET DENOISING AND JPEG2000 COMPRESSION
    (Elsevier Gmbh, 2020) Gungor, Murat Alparslan; Gencol, Kenan
    Image compression has significant importance due to broad employment of image data in today's computing and communication systems. In image compression, there is generally a trade-off between image visual quality and the compression rate. Although certain types of applications favor the visual quality of an image, some others can favor the compression rate. Thus, an optimal operating point between these two should be determined. In this study, for this aim, we propose an optimal compression procedure based on transform coding for noisy images. We combine the wavelet-based JPEG2000 compression algorithm with wavelet-based denoising algorithms. Thus, thanks to our procedure, optimum performance can be achieved depending on the image type.