Images taken by digital cameras include noise. The image quality is reduced with increasing noise. In addition, the image recognition rate decreases with increasing noise. Currently, image recognition is used in security technology through face recognition and in image inspection at production sites. Therefore, the accuracy of image recognition needs to be improved. Reducing noise is essential to improve the accuracy of image recognition. Low-pass filters such as a Gaussian filter (GF), are often used to reduce noise from images. Low-pass filters can reduce noise, however low-pass filters always blur the edges. As the edge blur becomes stronger, the accuracy of edge and feature detection of image recognition worsens. In order to solve this problem, a non-local mean filter (NLMF) was proposed as noise reduction filter for images that can preserve edges in previous research. The NLMF has high denoising performance against weak noise, while low denoising performance against strong noise. Therefore, in this research, we propose a noise reduction filter for images that can preserve edges which combining the GF and L2-norm. The proposed method is expected to simultaneously achieve high denoising and edge preservation performances against weak and strong noise. Therefore, the proposed method is expected to improve the image quality and, consequently, the accuracy of image recognition.
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Proposal of Edge Preserving Image Noise Reduction Filter for Using L2-Norm
Published: 15 October 2021 by MDPI in 2nd International Electronic Conference on Applied Sciences session Computing and Artificial Intelligence
Keywords: digital image processing; noise reduction; edge preserving; L2-norm; gaussian filter