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A study on image area analysis and improvement using denoising technique

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초록

Recently, various display products are being applied to automobiles. In the process of acquiring an image froma display product, a large amount of additive white Gaussian noise(AWGN) is generated. Generally knowndenoising techniques focus on removing noise, so detailed components including image information areproportionally lost in the process of removing noise. The algorithm proposed in this paper proposes a method toeffectively remove noise while preserving the detail of image information.

Abstract

Recently, various display products are being applied to automobiles. In the process of acquiring an image froma display product, a large amount of additive white Gaussian noise(AWGN) is generated. Generally knowndenoising techniques focus on removing noise, so detailed components including image information areproportionally lost in the process of removing noise. The algorithm proposed in this paper proposes a method toeffectively remove noise while preserving the detail of image information.

참고문헌 (6)

  1. Y. Tsin, "Statistical calibration of CCD image process", Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 1, pp.480~487 (2001)
  2. C. Tomasi, "Bilateral filtering for gray and color images", Sixth International Conference on Computer Vision, pp.839~846 (1998)
  3. P. Perona, "Scale-space and edge detection using anisotropic diffusion", IEEE Transactions Pattern Analysis and Machine Intelligence, 12(7), pp.629~639 (1990)[DOI]
  4. A. Buades, "A non-local algorithm for image denoising", 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR05), 2, pp.60~65 (2005)
  5. HONG-DOU REN, "Efficient edge detection based on ghost imaging", The Optical Society, 2(1) (2019)[DOI]
  6. Zhou Wang, "Image quality assessment:from error visibility to structural similarity", IEEE Transactions on Image Processing, 13(4), pp.600~612 (2004)[DOI]

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