This study involves the use of high-resolution airborne magnetic data to evaluate thicknesses sedimentary series in Bornu Basin, Northeast Nigeria, using three depth approximation techniques (source parameter imaging, standard Euler deconvolution, and 2D GM-SYS forward modelling methods). Three evenly spaced profiles were drawn N-S direction on total intensity map perpendicular regional structu...

We enhance the performance of the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image deconvolution. The original cost function is modified to overcome the problem of operation on images with different scales for the representation of pixel intensity levels. Algorithm resetting is used to enhance the convergence of the conjugate gradient algorit...

Ultrasonic imaging is a powerful nondestructive evaluation (NDE) tool for flaw characterization. This thesis discusses the signal processing techniques developed for ultrasonic imaging of flaws in composite laminates. Commonly used time-domain signal processing techniques have problems including poor resolution, dependence on operator's experiences and subjectivity. Some transform-domain proces...

In image acquisition, the captured image is often the result of the object being convolved with a blur function. Deconvolution is necessary in order to undo the effects of the blur. However, in real life we may have very little knowledge of the blur, and therefore we have to perform blind deconvolution. One major challenge of existing iterative algorithms for blind deconvolution is the enforcem...

Uniform confidence bands for densities f via nonparametric kernel estimates were first constructed by Bickel and Rosenblatt [Ann. Statist. 1, 1071–1095]. In this paper this is extended to confidence bands in the deconvolution problem g = f ∗ ψ for an ordinary smooth error density ψ. Under certain regularity conditions, we obtain asymptotic uniform confidence bands based on the asymptotic distri...

A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via deconvolutional inference. To speed up this inference, a new...

We provide an IDL implementation of Fourier deconvolution and apply it to data from a WFC3 H1RG near-infrared array detector that exhibits inter-pixel capacitance (IPC). The deconvolution removes the most obvious deleterious effect of IPC: the cross-like pattern (i.e. the arithematic symbol for addition, “+”) of charge around pixels with large dark current, a.k.a. “hot” pixels. We also exhibit ...

Deconvolution is typically used to sharpen fluorescence images, but when the signal-to-noise ratio is low, the primary benefit is reduced noise and a smoother appearance of the fluorescent structures. 3D time-lapse (4D) confocal image sets can be improved by deconvolution. However, when the confocal signals are very weak, the popular Huygens deconvolution software erases fluorescent structures ...

The probe correction technique applied to reactive near field characterization is based on a deconvolution process. However, the classical deconvolution based on an inverse Fourier transform has a restrictive limitation. It is based on the use of noiseless measurement data. Consequently, measurement noise makes the result obtained by the classical deconvolution based technique inefficient and r...