Auto-encoder is a special kind of neural network based on reconstruction. De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the original input by minimizing the reconstruction error function. And contractive auto-encoder (CAE) is another kind of improved auto-encoder to learn robust feature by int...

Unlike Gaussian noise, Rician noise filtering is more challenging, since this type of noise exists in Functional Magnetic Resonance Imaging (fMRI) data which makes the analysis of fMRI data very difficult for experimental and clinical purposes. To cope with the situation, normally (smoothing) de-noising is done before the analysis of the data using conventional methods like Gaussian filtering a...

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, dis...

Nowadays, most analytical instruments in modern laboratories are computerized, partly owing to the rapid development of advanced micro-electronic technology. Digitalized spectroscopic data can be exported from these instruments very easily for subsequent signal processing. Raman Spectroscopy is widely recognized as powerful, non-destructive techniques for characterizing materials. The key to re...

The process of removing noise from the original image is still a demanding problem for researchers. There have been several algorithms and each has its assumptions, merits, and demerits. The prime focus of this paper is related to the pre processing of an image before it can be used in applications. The pre processing is done by de-noising of images. In order to achieve these de-noising algorit...

Digital mammograms are coupled with noise which makes de-noising a challenging problem. In the literature, few wavelets like daubechies db3 and haar have been used for de-noising medical images. However, wavelet filters such as sym8, daubechies db4 and coif1 at certain level of soft and hard threshold have not been taken into account for mammogram images. Therefore, in this study five wavelet f...

The details of an image with noise may be restored by removing noise through a suitable image de-noising method. In this research, a new method of image de-noising based on using median filter (MF) in the wavelet domain is proposed and tested. Various types of wavelet transform filters are used in conjunction with median filter in experimenting with the proposed approach in order to obtain bett...

The de-noising of sensor data has become an important to research. Since the traditional de-noising method can’t achieve successful de-noising effect and the software-only method never meets a high real time capability. In this paper, we illustrate a novel threshold function based on the wavelet hard and soft threshold function. It is unlike ordinary function, which has overcome the defect such...

De-noising and De-blurring is the technique that uses procedures to subsidize and remove the strange and useless content in the images De-noising and de-blurring is very beneficial for detecting the diseases like cancers, tumors in human body. But noise and Blur are the major factors that degrade the quality of images and makes difficult to diagnose. Reconstructing the images is the way to over...

Image noise always affects the clarity of the image and leads to blurring. However, the traditional de-noising methods cannot effectively remove the image noise especially the noise in regions contains rich detail information. In order to eliminate the noise, preserve better detail information of the images and enhance the de-noising effect of Contourlet wavelet transformation for images and cu...