Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19964
標題: 使用多重解析度區域極值濾波器之消除影像高ISO雜訊演算法
High ISO Noise Reduction Using Multiresolution Local Extrema Filtering
作者: 楊思瑩
Yang, Ssu-Ying
關鍵字: 雜訊去除
noise reduction
高ISO雜訊
多重解析度
區域極值濾波器
小波轉換
high ISO noise
local extrema filtering
multiresolution
wavelet thresholding
出版社: 資訊科學與工程學系所
引用: [1] I. Pitas and A. N. Venetsanopoulos, “Nonlinear mean filters in image processing,” IEEE Trans. Acoust., Speech, Signal Processing vol. ASSP-34, pp.573-584, June 1986 [2] K. Arakawa, “Median filter based on fuzzy rules and its application to image restoration,” Fuzzy Sets and Systems, vol. 77, pp. 3-13, 1996 [3] C. Tomasi and R. Manduchi, “Bilateral filter for gray and color images,” in Proc. IEEE Int. Conf. Computer Vision, pp. 839–846,1998. [4] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” presented at the IEEE Computer Vision and Pattern Recognition,San Diego, CA, Jun. 2005. [5] A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005. [6] H. Faraji, W. J. MacLean, "CCD noise removal in digital images," IEEE Transactions of Image Processing, vol.15, no.9, p. 2676~2685, Sept. 2006 [7] R. Gheorghe, S. Goma, and M. Aleksic, “An image noise filter with emphasis on low frequency chrominance noise,” in Proc. SPIE Electronic Imaging Conference: Digital Photography V, vol. 7250, pp.72500B:1–10, San Jose, CA, Jan. 2008. [8] D. Krishnan and R. Fergus. Dark flash photography. In ACM Trans. Graphics, pages 1–11, 2009. [9] M. Zhang and B. K. Gunturk, “Multiresolution bilateral filtering for image denoising,” IEEE Trans. Image Processing, vol. 17, no. 12, pp. 2324–2333, Dec. 2008. [10] S. H. Lim, R. Maurer, and P. Kisilev, “Denoising scheme for realistic digital photos from unknown sources,” in Proc. Int. Conf. Acoustics,Speech, Signal Processing, pp. 1189–1192, Taipei, Taiwan, Apr. 2009. [11] Y.-I. Pyo, R.-H. Park, and S. Chang, "Noise reduction in high-ISO images using 3-D collaborative filtering and structure extraction from residual blocks," IEEE Trans. Consumer Electronics, vol. CE-57, no. 2, pp. 687-695, May 2011. [12] S.Arivazhagan ,N.Sugitha , and M.Vijay ," Adaptive Spatial and Multiresolution Approach for Image Denoising", in Proc. IEEE Int. Conf. Signal Processing, 2011. [13] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994. [14] K. Subr, C. Soler, and F. Durand, “Edge-preserving multiscale image decomposition based on local extrema,” in SIGGRAPH Asia, Singapore, 2008.
摘要: 數位影像在拍攝的過程中往往會因為相機本身電子元件或是外在因素如溫度、光線等問題而產生雜訊,而為了因應這些雜訊問題,許多濾除雜訊的方法也陸續被提出,在此本篇研究則是針對高ISO雜訊進行處理。高ISO雜訊可以分成低頻雜訊跟高斯雜訊,在影像中具有較粗的粒子狀的彩色雜訊,我們稱之低頻雜訊,由於低頻雜訊不易與影像真實訊號分離,因此後來發展的處理方式,使用了多重解析度的架構,將影像同時在低解析度跟高解析度下進行作用,利用不同解析度的特性濾除雜訊,使用小波轉換分別對高頻以及低頻的影像做濾波,目的在於有效的濾除影像中的雜訊,在去除雜訊的同時還能有效的保留影像細節與邊緣。 因此本篇研究提出一個多重解析度架構的區域極值濾波器,相對於之前使用雙向濾波器更能夠將雜訊與影像分離,另外我們使用兩階層的區域極值濾波器,被分離出來的細節部分中包含一些影像的邊緣架構以及雜訊,將這些邊緣部分擷取出來並加回初次去雜訊後的影像,更能夠保留住影像細節與邊緣的部分。從實驗結果來看,本方法不但能夠有效的濾除低頻雜訊,也能有效保留影像邊緣與細節。
Digital images are often corrupted by noise during image captured by digital cameras due to many factors, including temperature, exposure time encountered or image sensor (e.g. CCD). To overcome this problem, many methods were proposed. In this study, we focus on high ISO noise. High ISO noise image are affected by Gaussian noise and low frequency noise. It has a coarse-grain noise characteristics that called low frequency noise. It is difficult to distinguish between real signal and low frequency noise. To reduce low frequency noise, a denoising method is proposed to use multiresolution framework. Therefore, it have been used to analyze image at low resolution layer and high resolution layer . To use multiresolution characteristics to eliminate noise. Thus, using the wavelet transform to decompose the noise image into low and high frequency components and reduce noise, respectively. To reduce noise effectively and preserve image edge. In this study , we propose a multiresolution local extrema filtering. It can separates into noise and image effectively than bilateral filter. And we use a two-level extrema-based multi-scale decomposition(EMD) framework to extract more detail. The detail layer contains edge as well as noise.The edge extraction was added to the initial denoised image. In experiment result, we can observe that our proposed method efficient remove noise while preserving image edge.
URI: http://hdl.handle.net/11455/19964
其他識別: U0005-2407201216463400
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2407201216463400
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