There are many unavoidable noise interferences in image acquisition and transmission. To make it better for subsequent processing, the noise in the image should be removed in advance. There are many kinds of image noises, mainly including salt and pepper noise and Gaussian noise. This paper focuses on the research of the Gaussian noise removal. It introduces many wavelet threshold denoising alg...

Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coeecients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. In this paper, we develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs). The framework enables us to concisely model the ...

Abstract— Electrocardiography (ECG) signals are important in medical engineering to determine the condition of the heart. The proper processing of ECG signal and its accurate detection is very much essential for easy diagnosis. Generally ECG gets corrupted by noise and human artifacts. The denoising of this signal is very important issue in medical field. In this proposed work concentrated on d...

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods....

In electron tomographic reconstructions of biological specimens the information about their structure is not directly accessible since most of the signal is buried in noise. An interpretation of the images using surface and volume rendering techniques is difficult due to the noise sensitivity of rendering algorithms. We explore the use of various multiscale representations for denoising 2D and ...

Abstract In ultrasonic testing of coarse-grained materials, signal to noise ratio (SNR) of testing signals is reduced seriously for the structure noise, and echoes from defects are difficult to be identified. In order to improve the SNR and the reliability of ultrasonic testing of coarse-grained materials, empirical mode decomposition (EMD) is introduced to process the testing signal here. Sign...