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Advances in engineering software

Non-local means (NLM) filter is one of the state-of-the-art denoising filters. It exploits the presence of similar features in an image and averages those similar features to remove noise. However, a conventional NLM filter shows somewhat inferior performance of noise reduction around edges, suffering from low efficiency of collecting similar features to be averaged. In order to overcome this phenomenon, we propose a NLM filter with double Gaussian anisotropic kernels as a substitute for the conventional homogeneous kernel to effectively remove noise from OCT images corrupted by speckle noise. The proposed filter was evaluated by comparing with various denoising filters such as conventional NLM filter, median filter, bilateral filter, and Wiener filter. The fingertip OCT images, which were processed with the different denoising filters, indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI). A human retina OCT image was also used to compare and show the performances of noise reduction among different filters. In addition, the denoising performance with the proposed NLM filter was also investigated in the synthetic images for fair comparison among the filters by calculating the peak signal-to-noise ratio (PSNR). The proposed NLM filter outperformed the conventional NLM filter as well as the other filters.

Effective speckle noise suppression in optical coherence tomography images using nonlocal means denoising filter with double Gaussian anisotropic kernels

Semiconductor manufacturing processes are prone to process deviations or other production issues. Quality assurance of every processing step and measuring wafer test values is crucial for finding possible root causes of these problems. Automated visual inspection and recognition of patterns in wafermap data obtained during different processing steps has a potential to signifficantly improve the efficiency of finding early production issues and even help with adjustment of the production parameters to automatically resolve them. In this paper, we present a machine learning approach for unsupervised clustering of spatial patterns in wafermap measurement data. Measured test values are first pre-processed using some computer vision techniques, followed by a feature extraction based on variational autoencoders to decompose high-dimensional wafermaps into a low-dimensional latent representation. Final step is to detect the structure of this latent space and assign individual wafers into clusters. We experimentally evaluate the performance of the proposed method over a real dataset.

Unsupervised Wafermap Patterns Clustering via Variational Autoencoders

 Abstract— Kidney ultrasound imaging can be used to estimate kidney size and position, and help to diagnose structural abnormalities as well as the presence of cysts and stones. However, due to the presence of speckle noise in ultrasound images, performing the segmentation methods for the kidney images were very challenging and therefore, deleting and removing the complicated background will speeds up and increases the accuracy of the segmentation process. However, in previous studies, the ROI of the kidney is manually cropped. Therefore, this study proposed an automatic region of interest (ROI) generation for kidney ultrasound images. Firstly, some techniques of speckle noise reduction were implemented consist of median filter, Wiener filter and Gaussian low-pass filter. Then texture analysis was performed by calculating the local entropy of the image, continued with the threshold selection, morphological operations, object windowing, determination of seed point and last but not least the ROI generation. This method was performed to several kidney ultrasound images with different speckle noise reduction techniques and different threshold value selection. Based on the result, it shows that for median filter, threshold value of 0.6 gave the highest TRUE ROIs which were 70%. For Wiener filter, threshold value of 0.8 gave highest TRUE ROIs which were 80% and for Gaussian low-pass filter, threshold value of 0.7 gave highest TRUE ROIs which were 100%. By using the previous result, this method has been tested also to more than 200 kidney ultrasound images. As the result, for longitudinal kidney images, out of 120 images, 109 images generate true ROI (91%) and another 11 images generate false ROI (9%). For transverse kidney images, out of 100 images, 89 images generate true ROI (89%) and 11 images generate false ROI (11%). To conclude, the method in this study can be practically used for automatic generation of US kidney ROI.

/pdf/automatic-generation-of-region-of-interest-for-kidney-4ydw63zzf2.pdf

Automatic generation of region of interest for kidney ultrasound images using texture analysis

Expert system applied for classifying self-compacting concrete surface finish

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00162122/document

W. Puech

Papers

Short Communication: Digital image restoration by Wiener filter in 2D case