| 基于噪声检测和动态窗口的图像去噪算法 |
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| 作者姓名： | 王文豪 严云洋 姜明新 高尚兵 于永涛 |
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| 作者单位： | 淮阴工学院计算机与软件工程学院,江苏淮安,223003;淮阴工学院计算机与软件工程学院,江苏淮安,223003;淮阴工学院计算机与软件工程学院,江苏淮安,223003;淮阴工学院计算机与软件工程学院,江苏淮安,223003;淮阴工学院计算机与软件工程学院,江苏淮安,223003 |
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| 基金项目： | 国家自然科学基金项目(61603146)；江苏省六大人才高峰项目(XYDXXJS-012)；江苏省高等学校自然科学研究重大项目(18KJA520002)； 江苏省自然科学基金项目(BK20171267)；淮安市科技计划项目(HAP201605，HAA201738)；江苏省第五期333高层次人才培训项目 (BRA2018333) |
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| 摘 要： | 针对中值滤波算法在去除脉冲噪声时易造成图像细节丢失的问题，提出了一种基 于噪声检测和动态窗口的自适应滤波方法。首先借鉴 BDND 方法，将图像的像素初分成信号点 和疑似噪声点，以减少需要处理的像素点；然后设计一种窗口自适应的噪声检测方法对疑似噪 声点进一步检测，判断其是真噪声点还是细节点，以加强图像细节信息的保护；最后通过改进 的自适应中值滤波器滤除检测出的噪声，并融入窗口自适应控制，窗口的大小可以根据噪声情 况自适应地调整，在去除噪声的同时尽可能地保护图像细节。实验表明，该算法在噪声处理和 细节保护上要优于其他典型算法，能有效地提高图像的峰值信噪比，对于高密度噪声的图像， 也可以获得较好的去噪效果。
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| 关 键 词： | 脉冲噪声 噪声检测 自适应窗口 噪声去除 |
Image Denoising Algorithm Based on Noise Detection and Dynamic Window |
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| Authors: | WANG Wen-hao YAN Yun-yang JIANG Ming-xin GAO Shang-bing YU Yong-tao |
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| Affiliation: | Faculty of Computer & Software Engineering, Huaiyin Institute of Technology, Huai’an Jiangsu 223003, China |
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| Abstract: | Aimed at the loss of image details caused by median filtering when removing the impulse noise, this study proposes an adaptive filtering method based on noise detection and dynamic window. Firstly, using BDND method, the image pixel is divided into signal points and suspected noise points to reduce the number of pixels that need to be processed. Then a method of the window adaptive noise detection is designed to further distinguish the suspected noise points into the noise points and the fine points, which strengthen the protection of the details of the image. Finally the detected noise is removed by an improved adaptive median filter. The window adaptive control is integrated into this filter algorithm. The size of the window can be adaptively adjusted according to the noise condition. The image details are protected as much as possible while removing the noise. The experiments show that the algorithm in this paper is superior to other conventional algorithms in noise removal and detail protection, and can effectively improve the peak signal to noise ratio of the image. This method can also obtain better denoising effect for images with high density noise. |
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| Keywords: | impulse noise noise detection adaptive window noise removal |
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