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

By Leonidas Deligiannidis, Hamid R. Arabnia

Rising tendencies in picture Processing, computing device imaginative and prescient, and trend reputation discusses the newest in traits in imaging technological know-how which at its center involves 3 intertwined laptop technological know-how fields, specifically: photograph Processing, laptop imaginative and prescient, and trend reputation. there's major renewed curiosity in each one of those 3 fields fueled via enormous information and information Analytic tasks together with yet now not constrained to; purposes as diversified as computational biology, biometrics, biomedical imaging, robotics, safeguard, and information engineering. those 3 center themes mentioned the following offer a pretty good advent to picture processing besides low-level processing thoughts, computing device imaginative and prescient basics in addition to examples of utilized functions and development popularity algorithms and methodologies that might be of worth to the picture processing and computing device imaginative and prescient examine groups.

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Additionally, the difference image is colored, which means that the error is an offset and thus the color of the denoised image does not correspond to the correct color of the reference. The difference image in Figure 2 (c) shows the difference for the proposed Bayer SA-DCT. The image is most similar to noise, thus it looks more random, which means that not much image structure is lost and no color shift must be expected. Analyzing the results visually, we found that our method performs better due to the low spatial correlation of the error: the remaining denoising error appears less disturbing as it is less correlated.

Jong Hyuk) Park, Department of Computer Science and Engineering (DCSE), SeoulTech, Korea; President, FTRA, Editor-in-Chief, HCIS Springer, JoC, IJITCC; and Head of DCSE, SeoulTech, Korea Dr. Muhammad Imran Razzak, Department of Health Informatics, College of Public Health & Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Kingdom of Saudi Arabia Prof. Gerald Schaefer, Loughborough University, Loughborough, UK Prof. Fernando G. 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.

Our method requires only a local neighborhood for the denoising step and is therefore better suited for stream-based processing. Additionally it operates on Bayer data and the raw Bayer data has only one value per pixel whereas the processed RGB data has three values per pixel. Thus, a three times lower complexity can be expected compared to algorithms that require the fully processed image data. While the temporal denoising step implementation requires additional frame buffers, the computational cost of the calculation is kept very low.

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