Dynamic Decomposition of Spatiotemporal Neural Signals

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals

A computer implementation of an orthonormal expansion method for digital image noise suppression

Images are usually corrupted by noise which comes from various sources: noise in the recording media (e.g. film grain noise), and noise introduced in the transmission channel. Noise degrades the visual quality of images and obscures the detail information in the images. One of the major sources of noise for images recorded on films is film grain noise. An orthonormal expansion algorithm for digital image noise suppression is implemented. The objective is to preserve as much sharpness and produce as few artifacts in the processed image as possible. The method sections an image into non-overlapping blocks. Each block is treated as a matrix which is decomposed as a sum of outer products of its singular vectors. The coefficient of each outer product is modified by a scaling function and the matrix is reconstructed. The resulting image shows a reduction of noise. The two major problems in the method are: 1. the blocking artifacts due to the sectioned processing, and, 2. the trade-off between the suppression of noise and the loss of sharpness. By separating the image into the low frequency and the high frequency components and processing only the latter component, the method is able to reduce the blocking artifacts to an invisible level. To obtain the optimal trade-off between the suppression of noise and the loss of sharpness, systematic variations of the coefficient scaling function were used to process the image. The best choice of the scaling function is found to be [ 1 - (σi / ai ) 3 ] which is a little different from the least-square-error estimate, [ 1 - (σi / ai ) 2 ]

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.pd

A study of wavelet-based noise reduction techniques in mammograms

Breast cancer is one of the most common cancers and claims over one thousand lives every day. Breast cancer turns fatal only when diagnosed in late stages, but can be cured when diagnosed in its early stages. Over the last two decades, Digital Mammography has served the diagnosis of breast cancer. It is a very powerful aid for early detection of breast cancer. However, the images produced by mammography typically contain a great amount noise from the inherent characteristics of the imaging system and the radiation involved. Shot noise or quantum noise is the most significant noise which emerges as a result of uneven distribution of incident photons on the receptor. The X-ray dose given to patients must be minimized because of the risk of exposure. This noise present in mammograms manifests itself more when the dose of X-ray radiation is less and therefore needs to be treated before enhancing the mammogram for contrast and clarity. Several approaches have been taken to reduce the amount of noise in mammograms. This thesis presents a study of the wavelet-based techniques employed for noise reduction in mammograms --Abstract, page iii

Image quality optimization, via application of contextual contrast sensitivity and discrimination functions

What is the best luminance contrast weighting-function for image quality optimization? Traditionally measured contrast sensitivity functions (CSFs), have been often used as weighting-functions in image quality and difference metrics. Such weightings have been shown to result in increased sharpness and perceived quality of test images. We suggest contextual CSFs (cCSFs) and contextual discrimination functions (cVPFs) should provide bases for further improvement, since these are directly measured from pictorial scenes, modeling threshold and suprathreshold sensitivities within the context of complex masking information. Image quality assessment is understood to require detection and discrimination of masked signals, making contextual sensitivity and discrimination functions directly relevant. In this investigation, test images are weighted with a traditional CSF, cCSF, cVPF and a constant function. Controlled mutations of these functions are also applied as weighting-functions, seeking the optimal spatial frequency band weighting for quality optimization. Image quality, sharpness and naturalness are then assessed in two-alternative forced-choice psychophysical tests. We show that maximal quality for our test images, results from cCSFs and cVPFs, mutated to boost contrast in the higher visible frequencies