De-noising algorithms based on wavelet thresholding replace small wavelet coeecients by zero and keep or shrink the coeecients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use Generalized Cross Validation. This procedure is fast and does not require an estimation fo...

Fourier transform is applied to detect the direction of stripe noise before de-noising, which is advantageous for selecting the corresponding detail coefficients for threshold quantization after stationary wavelet transform. Depending on the direction of stripe noise, the corresponding detail coefficients contain stripe noise need to be removed, while retaining the approximate coefficients and ...

The use of electrocardiogram (ECG) plays significant role in the diagnosis of heart disease and human computer interface etc. But, the ECG signals get affected from different types of noise during data acquisition due to which it faces the problem to detect actual abnormality. De-noising of the ECG signal is so indispensable and for de-noising it various researcher work in this area. In this wo...

129 www.scientistlink.com Abstract: A physiological signal monitoring system and alerting system using wireless technology is presented. The two types of physiological signal monitoring are captured from the body through leads and using the radio-frequency transmitting and receiving module the data are interfaced to computer systems. Furthering using a developed user interface module the captur...

Partial discharge (PD) activity measurements have been carried out by selecting noise signals (de-noising) using Support Vector Machine (SVM)and then recognized Convolutional Neural Network (CNN). CNN testing was various models such as activation methods: Sigmoid, Softmax, Relu, Tanh, Swish. Number of layers used is 1, 2, 3, 4 with filter sizes 32, 64, 128, 256 and kernel 3x3, 2x2, 1x1, 1x2, 1x...

With the increasing demands of precise positioning in weak signal environment, high sensitive GNSS receiver research and development has been pushed badly in need. Conventional GNSS signal acquisition techniques are considered inadequate when the incoming signal is too weak. In this paper we have mainly consider wavelet denoising algorithm applying in weak GNSS signal acquisition. Conventional ...

Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in images. Image de-noising has become an integral part image processing workflow. It is used attenuate noises and accentuate specific within. Machine learning important tool image-de-noising workflow terms its robustness, accuracy, time requirement. This paper explores numerous state-of-the-art ma...

1 PG Scholar , Paavai Engineering College, Namakkal, 2 M .E., Associate professor ,Department of ECE, Paavai engineering college,Namakkal Received 25 November 2015; Accepted 4 December 2015 ABSTRACT: Single image haze removal has been a demanding problem due to its ill-posed nature. Images captured in hazy weather environment often suffer from poor illumination conditions that will create a lot...

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distri...

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging denoising as a hot area of research and also ima...