Abstract not found

As an attempt to introduce Interactive, Content Dependent Adaptive (ICDA) image processing a simple but powerful active image sensing and two image-enhancement methods are introduced via adaptive CNN-UM sensorcomputers. Thus the method ICDA can be used for adaptive control of image sensing and for subsequent on-line or offline image enhancement as well. The algorithms use both intensity and contrast content. The image sensing technology can be realized with the current CNN-UM chip [1],[2]. Our first image enhancement method is also executable on this chip, but it is more suitable for the Adaptive Cellular Neural Network Universal Machine (ACNN-UM) architecture [3]. Some results of simulator and chip experiments and an adaptive extended cell is presented. Our second, dynamical image enhancement method is planned to be executable on a multi-layer, complex cell CNN architecture [3]. In [15] a 3-layer architecture is described which is capable to realize the main part of the second enhancement method. The main issues of our paper are as follows: the novel outlook of the ICDA framework, 3 new methods for two key application-area of CNN-UM, the notion of “regional ” adaptive computing, the novelty of application of equilibriumcomputing in the third method. However, the key novelty of our work is not just a new method and a new realization: by combining sensing and computing, dynamically and pixelwise, a new quality becomes practical. 1.

Introduction In this chapter we shall lay bare the theory and implementation details of hexagonal sampling systems and hexagonal quadrature mirror filters (HQMF). Hexagonal sampling systems are of particular interest because they exhibit the tightest packing of all regular two-dimensional sampling systems and for a circularly band-limited waveform, hexagonal sampling requires 13.4 percent fewer samples than rectangular sampling [1]. In addition, hexagonal sampling systems also lead to nonseparable quadrature mirror filters in which all basis functions are localized in space, spatial frequency and orientation [2]. This chapter is organized in two sections. Section I describes the theoretical aspects of hexagonal sampling systems while Section II covers important implementation details. I. Hexagonal sampling system This section presents the theoretical foundation of hexagonal sampling systems and hexagonal quadrature mirror filters. Most of this material has ap

Analyses based on Symmetric Daubechies Wavelets (SDW) lead to complex-valued multiresolution representations of real signals. After a recall of the construction of the SDW, we present some specific properties of these new types of Daubechies wavelets. We then discuss two applications in image processing: enhancement and restoration. In both cases, the efficiency of this multiscale representation relies on the information encoded in the phase of the complex wavelet coefficients. 1. INTRODUCTION Many current investigations in mathematical imaging consist in finding the optimal representation to perform specific enhancements by extracting the relevant information contained in an empirical signal. This question of representation, present in many fields of applied mathematics and physics, is indeed the cornerstone of the pionnering work of D. Marr in vision 1 . The "primal sketch" of an image he proposed was based on the multiscale edge representation obtained through the action of some...

this paper, we propose a new class of interpolating wavelets, which are generated from a generalized, window-modulated interpolating shell. Taking advantage of various interpolating shells, such as Lagrange polynomials and the Sinc function, etc., bell-shaped, smooth window modulation leads to wavelets with arbitrary smoothness in both time and frequency. Our method leads to a powerful and easily implemented series of interpolating wavelet. Generally, this novel designing technique can be extended to generate other non-interpolating multiresolution analyses as well (such as the Hermite shell). Unlike the biorthogonal solution discussed in [6], we do not attempt to solve a system of algebraic equations explicitly. We first choose an updating filter, and then solve the approximation problem, which is a rth-order accurate 1 1

pr agt. Mehrschichtige Perzeptrons sind z.B. in [2] zur Detektion und Klassifikation von Mikrokalzifikationen eingesetzt worden. Die Wavelet-Transformation ist in CAD-Systemen haupts achlich zum Bildenhancement und zur Detektion von Mikrokalzifikationen eingesetzt worden. Die Verwendung der Wavelet-Transformation bei der Detektion von Massen wurde erstmals von Wei et al. vorgestellt [3]. Laine et al. [4] haben zum ersten Mal die Anwendung der Wavelet-Transformation f ur die Merkmalsextraktion bei Mammographie-Aufnahmen gezeigt. So k onnen uberabgetastete WaveletTransformationen bei jeder Skala die ortliche Lokalisation der Masse dank der Translationsinvarianz erhalten. 2 Das neuronale Radialbasisnetz Neuronale Netze mit Radialbasisklassifikatoren stellen einen universellen Approximator bei einem dreischichtigen Aufbau dar. Die Ein- und Ausgangsschicht bestehen aus linearen Einheiten. Die Neuronen der verborgenen Schicht bestimmen den euklidischen Abstand zwischen Ein

This study focuses on medical image analysis techniques used in radiological processes; specifically, the analysis of mammograms by computer-aided diagnosis systems that attempt to provide both sensitivity and specificity in the identification of anomalies in mammograms and other types of medical images. An assembly of the strongest techniques into a coherent CAD system is then proposed is Section 4. A large list of references are compiled at the end of this paper for further study, with links to printed materials where applicable.

In this paper we describe a fast approach for image contrast enhancement, based on localized contrast manipulation. Our approach is not only fast and easy to implement, but also has several other promising properties (adaptive, multiscale, weighted localization, etc.). We will also discuss in this paper an anisotropic version of our approach. Several examples of medical images, including brain MR images, chest CT images and mammography images, will be provided to demonstrate the performance of our approach.

Aiming at uneven illumination and low contrast of large plane array CCD images, this paper presents an effective algorithm to extract uneven illumination from images and enhance the contrast by using wavelet transform. Based on an analysis of the illumination model on a focal plane, emulational images are constructed for the illumination model. Through analysis of the wavelet transform coefficients of emulational images, it is found that information of illumination non-uniformity mainly appears in wavelet approximate coefficients, and detail information is contained in detail coefficients. Taking advantage of this property, the original image is first decomposed with multiresolution wavelet, next, the natural logarithms of approximate coefficients are calculated, and an appropriate attenuating operator is applied to implement the non-uniformity correction of approximate coefficients before exponentiating correction, linear contrast-stretch is applied to all the approximate coefficients, a nonlinear function is applied to each level detail coefficients to accomplish adaptive contrast enhancement, finally, uniform illuminative and contrast enhanced aerial images are acquired through reconstruction of images. The experiments show that the wavelet analysis method can achieve a satisfying effect of removing the uneven illumination for digital aerial images and keeping well detail image features, contrast enhancing aim is also achieved. 1.

A distinction is usually made between wavelet bases and wavelet frames. The former are associated with a one-to-one representation of signals, which is somewhat constrained but most efficient computationally. The latter are over-complete, but they offer advantages in terms of flexibility (shape of the basis functions) and shift-invariance. In this paper, we propose a framework for improved wavelet analysis based on an appropriate pairing of a wavelet basis with a mildly redundant version of itself (frame). The processing is accomplished in four steps: 1) redundant wavelet analysis, 2) wavelet-domain processing, 3) projection of the results onto the wavelet basis, and 4) reconstruction of the signal from its nonredundant wavelet expansion. The wavelet analysis is pyramid-like and is obtained by simple modification of Mallat’s filterbank algorithm (e.g., suppression of the down-sampling in the wavelet channels only). The key component of the method is the subband regression filter (Step 3) which computes a wavelet expansion that is maximally consistent in the least squares sense with the redundant wavelet analysis. We demonstrate that this approach significantly improves the performance of soft-threshold wavelet denoising with a moderate increase in computational cost. We also show that the analysis filters in the proposed framework can be adjusted for improved feature detection; in particular, a new quincunx Mexican-hat-like wavelet transform that is fully reversible and essentially behaves the th Laplacian of a Gaussian.