基于变换域技术的新型社会分组优化算法图像去噪

B. Sekhar, P V G D Prasad Reddy, S. Venkataramana, V. Chakravarthy, P Satish Rama Chowdary
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引用次数: 2

摘要

近年来,图像传播在医学、娱乐、游戏、邮件等许多领域得到了发展。因此,立即需要对接收到的图像进行降噪,因为在通信期间在信道中添加的噪声会改变或恶化图像中包含的信息。任何涉及图像去噪或去噪的图像处理技术都必须从空间域开始,以变换域结束。通过对均值滤波器、中值滤波器、拉普拉斯滤波器等不同图像滤波器的性能进行改进,在空间域中进行了大量的研究。近年来,利用进化计算工具对变换域下的变换技术进行了大量的研究。与传统的结合小波去噪技术相比,ECT在变换域具有优势。在本文中,作者将SGOA等新颖的ECT应用于单色和彩色图像的去噪问题,并使用几个图像质量指标对去噪性能进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Denoising Using Novel Social Grouping Optimization Algorithm with Transform Domain Technique
In recent days, image communication has evolved in many fields like medicine, entertainment, gaming, mail, etc. Thus, it is an immediate need to denoise the received image because noise that is added in the channel during communication alters or deteriorates information contained in the image. Any image processing techniques concerned with image denoising or image noise removal has to be started with the spatial domain and end with the transform domain. A lot of research was carried out in the spatial domain by modifying the performance of different image filters such as mean filters, median filters, Laplacian filters, etc. Recently much research was carried out in Transform techniques under the transform domain, with evolutionary computing tools (ECT). ECT has proven to be dominant when compared with traditional denoising techniques in combination with wavelets in the transform domain. In this article, the authors applied a novel ECT such as SGOA on the denoising problem for denoising monochrome as well as color images and performance for denoising was evaluated using several image quality metrics.
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