The novel noise classification techniques found on quadruple threshold statistical detection filter under fix intensity impulse outlier environment

Because of the enormous necessity of contemporary noise suppressing algorithms, this article proposes the novel noise classification technique found on QTSD filter improved from the TTSD filter. The four thresholds for each auxiliary situations are incorporated into the proposed QTSD framework for dealing with the limitation of the earlier noise classification technique. The mathematical pattern is modeled by each photograph elements and is investigated in contradiction to the 1st threshold for analyzing whether it is non-noise or noise photograph elements. Subsequently, the calculated photograph element is analyzed with the contradiction between the 2nd threshold, which is modeled by using the normal distribution (mean and variance), and is analyzed with the contradiction between the 3rd threshold, which is modeled by using the quartile distribution (median). Finally, the calculated photograph element is investigated in contradiction to the 4th threshold, which is modeled from maximum or minimum value for analyzing whether it is non-noise or noise photograph elements FIIN. For performance evaluation, extensive noisy photographs are made up of nine photographs under FIIN environment distribution, which are synthesized for investigating the proposed noise classification techniques found on QTSD filter in the objective indicators (noise classification, non-noise classification and overall classification correctness). From these results, the proposed noise classification technique can outstandingly produce the higher correctness than the earlier noise classification techniques.

Facial image noise classification and denoising using neural network

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.

Source Type Classification and Localization of Inter-Floor Noise with a Single Sensor and Knowledge Transfer between Reinforced Concrete Buildings

A convolutional neural network (CNN)-based inter-floor noise source type classifier and locator with input from a single microphone was proposed in [Appl. Sci.9, 3735 (2019)] and validated in a campus building experiment. In this study, the following extensions are presented: (1) data collections of nearly 4700 inter-floor noise events that contain the same noise types as those in the previous work at source positions on the floors above/below in two actual apartment buildings with spatial diversity, (2) the CNN-based method for source type classification and localization of inter-floor noise samples in apartment buildings, (3) the limitations of the method as verified through several tasks considering actual application scenarios, and (4) source type and localization knowledge transfer between the two apartment buildings. These results reveal the generalizability of the CNN-based method to inter-floor noise classification and the feasibility of classification knowledge transfer between residential buildings. The use of a short and early part of event signal is shown as an important factor for localization knowledge transfer.

3D Snow Sculpture Reconstruction Based on Structured-Light 3D Vision Measurement

Structured-light technique is an effective method for indoor 3D measurement, but it is hard to obtain ideal results outdoors because of complex illumination interference on sensors. This paper presents a 3D vision measurement method based on digital image processing to improve resistance to noise of measuring systems, which ensuresnormal operation of a structured-light sensor in the wild without changing its components, and the method is applied in 3D reconstruction of snow sculpture. During image preprocessing, an optimal weight function is designed based on noise classification and minimum entropy, and the color images are transformed into monochromatic value images to eliminate most environmental noise. Then a Decision Tree Model (DTM) in a spatial-temporal context of video sequence is used to extract and track stripe. The model is insensitive to stubborn noise and reflection in the images, and the result of the model after coordinate transformation is a 3D point cloud of the corresponding snow sculpture. In experimental results, the root mean square (RMS) error and mean error are less than 0.722 mm and 0.574 mm respectively, showing that the method can realize real-time, robust and accurate measurement under a complex illumination environment, and can therefore provide technical support for snow sculpture 3D measurement.