Download Emerging Trends in Image Processing, Computer Vision and by Leonidas Deligiannidis, Hamid R. Arabnia PDF

By Leonidas Deligiannidis, Hamid R. Arabnia

Rising developments in photograph Processing, machine imaginative and prescient, and trend popularity discusses the newest in traits in imaging technological know-how which at its center comprises 3 intertwined machine technological know-how fields, specifically: photograph Processing, desktop imaginative and prescient, and development reputation. there's major renewed curiosity in every one of those 3 fields fueled by way of sizeable info and knowledge Analytic tasks together with yet now not restricted to; functions as varied as computational biology, biometrics, biomedical imaging, robotics, safeguard, and data engineering. those 3 center themes mentioned right here offer an effective advent to snapshot processing in addition to low-level processing concepts, laptop imaginative and prescient basics besides examples of utilized functions and trend reputation algorithms and methodologies that would be of price to the picture processing and laptop imaginative and prescient study groups.

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Extra resources for Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

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The neighborhood estimation prevents from oversmoothing, which is the prevailing denoising drawback. Foi et al. adapted the method to signal-dependent noise [8], thus it can be easily used for the noise in raw data. However, the method is still not adapted to linear and CFA data. We, therefore, propose to adapt and extend the method. We calculate the neighborhood estimation on luminance data and we propose a luminance transformation that can be directly applied to the CFA data. Additionally, we show how to adapt the shape-adaptive DCT (SA-DCT) to Bayer data, as this is the most usual CFA, and describe how the real noise characteristics from a digital camera can be obtained and included in the method.

Tinetti, School of Computer Science, Universidad Nacional de La Plata, La Plata, Argentina and Co-editor, Journal of Computer Science and Technology (JCS&T) Prof. Vladimir Volkov, The Bonch-Bruevich State University of Telecommunications, Saint-Petersburg, Russia Prof. Patrick S. P. Wang, Fellow: IAPR, ISIBM, WASE; Professor of Computer and Information Science, Northeastern University, Boston, Massachusetts, USA and Zijiang Visiting Chair, ECNU, Shanghai, NTUST, Taipei; iCORE Visiting Professor, University of Calgary, Canada; and Otto-von-Guericke Distinguished Guest Professor, University Magdeburg, Germany Prof.

Therefore, we extend our method using an additional temporal denoising step. The flickering could be a consequence of the mean calculation, which dominates the result for large homogeneous areas during the denoising. Large homogeneous ~ + j, which results in a large threshold areas are likely to have maximum size jU x and hence the result is mostly dominated by the mean and the DC-coefficient. As the DC-coefficient is not noise-free, temporal flickering can be observed. To improve the visual quality in video sequences, we therefore propose to add an additional temporal denoising step.

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