Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

中文翻译：

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走向最佳小波去噪方案——基于小波的生物医学数据平滑的新型空间和体积映射。


小波变换是数据去噪、平滑、分解、特征提取和其他相关任务最常见的过程之一。为了执行此类任务，我们需要选择适当的小波设置，包括特定的小波、分解级别和其他参数，这些参数形成小波变换输出。由于缺乏适合小波设置的通用推荐工具，此类参数的选择是一个具有挑战性的领域。在本文中，我们提出了一种通用推荐系统，用于预测数据平滑的合适小波选择。所提出的系统旨在为选定的小波和分解级别生成空间响应矩阵。这种响应能够映射选定的评估参数，从而确定小波设置的功效。所提出的系统还能够通过使用体积响应来跟踪小波功效背景下的动态噪声影响。我们提供主要针对肌肉骨骼系统的计算机断层扫描 (CT) 和磁共振 (MR) 图像数据以及肌电图信号的测试，以客观化临床数据处理的系统可用性。实验测试采用MSE（均方误差）、ED（欧几里德距离）和Corr（相关指数）等评价参数进行。我们还提供基于 Mann-Whitney 检验的结果统计分析，该检验指出了被椒盐噪声和高斯噪声损坏的数据中各个小波的统计显着差异。