Abstract

Noise leads to the ambiguity in regions of interest detection by corrupting the pixel information and is a vital problem in image processing domain. A novel hybrid technique based on fuzzy filtering and fuzzy thresholding is proposed here to extract the object regions accurately in presence of Gaussian noises. The proposed method is automated, does not need any parameter tuning as well does not need prior knowledge of the image or noise. An asymmetrical triangular fuzzy filter with median center coupled with a thresholding based on fuzziness minimization technique are implemented for this purpose. The fuzzy thresholding technique helps to classify the pixels with low signal-to-noise ratio (SNR) caused either due to noise or by the application of noise removal process. The proposed technique is applied in benchmark images corrupted by noises and are compared with some of the popular algorithms of object detection. The results indicate that the proposed method has superior performance in terms of peak signal-to-noise ratio (PSNR) and mean square error (MSE) value for images corrupted with Gaussian noises with standard deviation upto 1.5.

中文翻译：

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噪声图像中感兴趣区域的混合模糊滤波-模糊阈值技术

摘要

噪声通过破坏像素信息而导致感兴趣区域检测中的歧义，并且在图像处理领域中是至关重要的问题。提出了一种基于模糊滤波和模糊阈值的新型混合技术，可以在高斯噪声存在的情况下准确地提取目标区域。所提出的方法是自动化的，不需要任何参数调整，也不需要图像或噪声的先验知识。为此，实现了一种不对称三角模糊滤波器，其中值中心与基于模糊最小化技术的阈值相结合。模糊阈值技术有助于对归因于噪声或由于应用噪声消除过程而导致的低信噪比（SNR）的像素进行分类。所提出的技术应用于被噪声破坏的基准图像，并与一些流行的目标检测算法进行了比较。结果表明，对于高斯噪声破坏的图像（标准偏差最大为1.5），该方法在峰值信噪比（PSNR）和均方误差（MSE）值方面均具有优异的性能。