The multi-fractal structure of contrast changes in natural images: from sharp edges to textures

We present a formalism that leads very naturally to a hierarchical description of the different contrast structures in images, providing precise definitions of sharp edges and other texture components. Within this formalism, we achieve a decomposition of pixels of the image in sets, the fractal components of the image, such that each set only contains points characterized by a fixed stregth of the singularity of the contrast gradient in its neighborhood. A crucial role in this description of images is played by the behavior of contrast differences under changes in scale. Contrary to naive scaling ideas where the image is thought to have uniform transformation properties \cite{Fie87}, each of these fractal components has its own transformation law and scaling exponents. A conjecture on their biological relevance is also given.Comment: 41 pages, 8 figures, LaTe

Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms

We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions--the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L^2(R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L^2(R^2), we then discuss a methodology for constructing 2D directional-selective complex wavelets. In particular, analogous to the HT correspondence between the components of the 1D counterpart, we relate the real and imaginary components of these complex wavelets using a multi-dimensional extension of the HT--the directional HT. Next, we construct a family of complex spline wavelets that resemble the directional Gabor functions proposed by Daugman. Finally, we present an efficient FFT-based filterbank algorithm for implementing the associated complex wavelet transform.Comment: 36 pages, 8 figure

Detection and estimation of image blur

The airborne imagery consisting of infrared (IR) and multispectral (MSI) images collected in 2009 under airborne mine and minefield detection program by Night Vision and Electronic Sensors Directorate (NVESD) was found to be severely blurred due to relative motion between the camera and the object and some of them with defocus blurs due to various reasons. Automated detection of blur due to motion and defocus blurs and the estimation of blur like point spread function for severely degraded images is an important task for processing and detection in such airborne imagery. Although several full reference and reduced reference methods are available in the literature, using no reference methods are desirable because there was no information of the degradation function and the original image data. In this thesis, three no reference algorithms viz. Haar wavelet (HAAR), modified Haar using singular value decomposition (SVD), and intentional blurring pixel difference (IBD) for blur detection are compared and their performance is qualified based on missed detections and false alarms. Three human subjects were chosen to perform subjective testing on randomly selected data sets and the truth for each frame was obtained from majority voting. The modified Haar algorithm (SVD) resulted in the least number of missed detections and least number of false alarms. This thesis also evaluates several methods for estimating the point spread function (PSF) of these degraded images. The Auto-correlation function (ACF), Hough transform (Hough) and steer Gaussian filter (SGF) based methods were tested on several synthetically motion blurred images and further validated on naturally blurred images. Statistics of pixel error estimate using these methods were computed based on 8640 artificially blurred image frames --Abstract, page iii

Direct Imaging of Graphene Edges: Atomic Structure and Electronic Scattering

We report an atomically-resolved scanning tunneling microscopy (STM) investigation of the edges of graphene grains synthesized on Cu foils by chemical vapor deposition (CVD). Most of the edges are macroscopically parallel to the zigzag directions of graphene lattice. These edges have microscopic roughness that is found to also follow zigzag directions at atomic scale, displaying many ~120 degree turns. A prominent standing wave pattern with periodicity ~3a/4 (a being the graphene lattice constant) is observed near a rare-occurring armchair-oriented edge. Observed features of this wave pattern are consistent with the electronic intervalley backscattering predicted to occur at armchair edges but not at zigzag edges

Detecting Activations over Graphs using Spanning Tree Wavelet Bases

We consider the detection of activations over graphs under Gaussian noise, where signals are piece-wise constant over the graph. Despite the wide applicability of such a detection algorithm, there has been little success in the development of computationally feasible methods with proveable theoretical guarantees for general graph topologies. We cast this as a hypothesis testing problem, and first provide a universal necessary condition for asymptotic distinguishability of the null and alternative hypotheses. We then introduce the spanning tree wavelet basis over graphs, a localized basis that reflects the topology of the graph, and prove that for any spanning tree, this approach can distinguish null from alternative in a low signal-to-noise regime. Lastly, we improve on this result and show that using the uniform spanning tree in the basis construction yields a randomized test with stronger theoretical guarantees that in many cases matches our necessary conditions. Specifically, we obtain near-optimal performance in edge transitive graphs, $k$-nearest neighbor graphs, and $\epsilon$-graphs

Multiscale analysis of waves reflected by complex interfaces: Basic principles and experiments

International audience[1] This paper considers the reflection of waves by multiscale interfaces in the framework of the wavelet transform. First, we show how the wavelet transform is efficient to detect and characterize abrupt changes present in a signal. Locally homogeneous abrupt changes have conspicuous cone-like signatures in the wavelet transform from which their regularity may be obtained. Multiscale clusters of nearby singularities produce a hierarchical arrangement of conical patterns where the multiscale structure of the cluster may be identified. Second, the wavelet response is introduced as a natural extension of the wavelet transform when the signal to be analyzed (i.e., the velocity structure of the medium) can only be remotely probed by propagating wavelets into the medium instead of being directly convolved as in the wavelet transform. The reflected waves produced by the incident wavelets onto the reflectors present in the medium constitute the wavelet response. We show that both transforms are equivalent when multiple scattering is neglected and that cone-like features and ridge functions can be recognized in the wavelet response as well. Experimental applications of the acoustical wavelet response show how useful information can be obtained about remote multiscale reflectors. A first experiment implements the synthetic cases discussed before and concerns the characterization of planar reflectors with finite thicknesses. Another experiment concerns the multiscale characterization of a complex interface constituted by the surface of a layer of monodisperse glass beads immersed in water. Citation: Le Gonidec, Y., D. Gibert, and J.-N. Proust , Multiscale analysis of waves reflected by complex interfaces: Basic principles and experiments

Study of Lipschitz regularity - Feature extraction on regular and irregular grids

In this paper we study the pointwise Lipschitz regularity, covering several aspects: theoretical and practical, methods for its estimation on regular and irregular grids. The relevance of this value of regularity lies in its invariance properties to several transformations, and its fast computation thanks to wavelets. We study the influence of scale on wavelets transforms and show invariance properties this value of regularity. We also put forward an original technique for its estimation on regular grids. We also address the issue of irregular grids, based on the behavior of smoothing kernels with respect to scale. The obtained results emphasize the usefulness of such features for the applications, and motivate further work on this topic. Keywords: Lipschitz regularity, wavelets, smoothing kernels, robust feature extraction, regular and irregular gridsJRC.G.2-Global security and crisis managemen

"Rewiring" Filterbanks for Local Fourier Analysis: Theory and Practice

This article describes a series of new results outlining equivalences between certain "rewirings" of filterbank system block diagrams, and the corresponding actions of convolution, modulation, and downsampling operators. This gives rise to a general framework of reverse-order and convolution subband structures in filterbank transforms, which we show to be well suited to the analysis of filterbank coefficients arising from subsampled or multiplexed signals. These results thus provide a means to understand time-localized aliasing and modulation properties of such signals and their subband representations--notions that are notably absent from the global viewpoint afforded by Fourier analysis. The utility of filterbank rewirings is demonstrated by the closed-form analysis of signals subject to degradations such as missing data, spatially or temporally multiplexed data acquisition, or signal-dependent noise, such as are often encountered in practical signal processing applications