Image Improvement Technique Using Feed Forward Neural Network

This research is aimed to develop an efficient image enhancement technique using multi layer Feedforward neural network. A nonlinear digital filter has been introduced as a promising solution for improving the image quality.The filter, which is named unsharp mask filter based neural network,significantly enhances the sharpness of image while highlights its details(edges and lines).In this thesis sharpening of image details has been obtained.Multi-layer Feed forward neural network with back propagation algorithm known as Multilayer Perceptron (MLP) is used to control the level of contrast enhancement.Grayscale blurred images were also used in this study.The results have been evaluated using mean square error as well as grayscale histogram distribution for sharpening of image details.Comparison among 3x3, 5x5 and 7x7 mask sizes has shown that least mean square error has been achieved by using the 3x3 mask size. However, the grayscale histogram distribution has shown that the proposed method has given more image details sharpening (11.333% in average)compared to the original free noise image.Regarding the size of filter mask, three filter masks which are, 3 x 3, 5 x 5 and 7 x 7 have been used in this study.Results have shown that the mean square error is proportionate with the increasing of mask size. The program has been implemented using MATLAB version 6.5 as programming language.Finally, unsharp mask filter based neural network with different mask sizes has been investigated. Results have shown that better performance has been obtained using the proposed method, i.e., 10% for 3x3, 11% for 5x5 and 13% for 7x7 mask size

Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement

The goal of many early visual filtering processes is to remove noise while at the same time sharpening contrast. An historical succession of approaches to this problem, starting with the use of simple derivative and smoothing operators, and the subsequent realization of the relationship between scale-space and the isotropic dfffusion equation, has recently resulted in the development of "geometry-driven" dfffusion. Nonlinear and anisotropic diffusion methods, as well as image-driven nonlinear filtering, have provided improved performance relative to the older isotropic and linear diffusion techniques. These techniques, which either explicitly or implicitly make use of kernels whose shape and center are functions of local image structure are too computationally expensive for use in real-time vision applications. In this paper, we show that results which are largely equivalent to those obtained from geometry-driven diffusion can be achieved by a process which is conceptually separated info two very different functions. The first involves the construction of a vector~field of "offsets", defined on a subset of the original image, at which to apply a filter. The offsets are used to displace filters away from boundaries to prevent edge blurring and destruction. The second is the (straightforward) application of the filter itself. The former function is a kind generalized image skeletonization; the latter is conventional image filtering. This formulation leads to results which are qualitatively similar to contemporary nonlinear diffusion methods, but at computation times that are roughly two orders of magnitude faster; allowing applications of this technique to real-time imaging. An additional advantage of this formulation is that it allows existing filter hardware and software implementations to be applied with no modification, since the offset step reduces to an image pixel permutation, or look-up table operation, after application of the filter

A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications