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首页> 外文期刊>Image Processing, IET >High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation
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High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation

机译：基于深度卷积神经网络和粒子群算法的高密度脉冲噪声检测与消除

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摘要

Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors' model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high-density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high-density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.
机译：大多数脉冲降噪器基于中值滤波器或基于模糊滤波器，它们只能在低噪声条件下表现良好。这项研究提出了一种高效的卷积神经网络（CNN）和粒子群优化（PSO）模型，用于高密度脉冲噪声去除。提出的高密度脉冲噪声检测与去除模型主要由两部分组成：脉冲噪声去除和用于恢复的脉冲噪声像素检测。作者的模型最初利用深层CNN架构的强大功能将噪声与噪声图像分离，然后采用PSO来确定用于检测脉冲噪声像素的最佳阈值。这些算法的组合是一种智能且自适应的解决方案，可在保持重要像素信息的同时产生干净的输出。为了解决高密度脉冲噪声问题，作者用大量的自然图像训练了他们的模型，并使用14张标准测试图像进​​行验证。为了验证所提出方法的鲁棒性，考虑了不同级别的高密度脉冲噪声。基于最终去噪的图像，他们的模型在灰度和彩色图像的视觉质量和定量评估方面都证明了其可靠性。

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