Seismic Fault Preserving Diffusion

This paper focuses on the denoising and enhancing of 3-D reflection seismic data. We propose a pre-processing step based on a non linear diffusion filtering leading to a better detection of seismic faults. The non linear diffusion approaches are based on the definition of a partial differential equation that allows us to simplify the images without blurring relevant details or discontinuities. Computing the structure tensor which provides information on the local orientation of the geological layers, we propose to drive the diffusion along these layers using a new approach called SFPD (Seismic Fault Preserving Diffusion). In SFPD, the eigenvalues of the tensor are fixed according to a confidence measure that takes into account the regularity of the local seismic structure. Results on both synthesized and real 3-D blocks show the efficiency of the proposed approach.Comment: 10 page

An underwater image enhancement by reducing speckle noise using modified anisotropic diffusion filter

Underwater images are usually suffering from the issues of quality degradation, such as low contrast due to blurring details, color deviations, non-uniform lighting, and noise. Since last few decades, many researches are undergoing for restoration and enhancement for degraded underwater images. In this paper, we proposed a novel algorithm using modified anisotropic diffusion filter with dynamic color balancing strategy. This proposed algorithm performs based on an employing effective noise reduction as well as edge preserving technique with dynamic color correction to make uniform lighting and minimize the speckle noise. Furthermore, reanalyze the contributions and limitations of existing underwater image restoration and enhancement methods. Finally, in this research provided the detailed objective evaluations and compared with the various underwater scenarios for above said challenges also made subjective studies, which shows that our proposed method will improve the quality of the image and significantly enhanced the image

Anisotropic enhanced backscattering induced by anisotropic diffusion

The enhanced backscattering cone displaying a strong anisotropy from a material with anisotropic diffusion is reported. The constructive interference of the wave is preserved in the helicity preserving polarization channel and completely lost in the nonpreserving one. The internal reflectivity at the interface modifies the width of the backscatter cone. The reflectivity coefficient is measured by angular-resolved transmission. This interface property is found to be isotropic, simplifying the backscatter cone analysis. The material used is a macroporous semiconductor, gallium phosphide, in which pores are etched in a disordered position but with a preferential direction

Optimized Anisotropic Rotational Invariant Diffusion Scheme on Cone-Beam CT

Cone-beam computed tomography (CBCT) is an important image modality for dental surgery planning, with high resolution images at a relative low radiation dose. In these scans the mandibular canal is hardly visible, this is a problem for implant surgery planning. We use anisotropic diffusion filtering to remove noise and enhance the mandibular canal in CBCT scans. For the diffusion tensor we use hybrid diffusion with a continuous switch (HDCS), suitable for filtering both tubular as planar image structures. We focus in this paper on the diffusion discretization schemes. The standard scheme shows good isotropic filtering behavior but is not rotational invariant, the diffusion scheme of Weickert is rotational invariant but suffers from checkerboard artifacts. We introduce a new scheme, in which we numerically optimize the image derivatives. This scheme is rotational invariant and shows good isotropic filtering properties on both synthetic as real CBCT data

Elimination of Glass Artifacts and Object Segmentation

Many images nowadays are captured from behind the glasses and may have certain stains discrepancy because of glass and must be processed to make differentiation between the glass and objects behind it. This research paper proposes an algorithm to remove the damaged or corrupted part of the image and make it consistent with other part of the image and to segment objects behind the glass. The damaged part is removed using total variation inpainting method and segmentation is done using kmeans clustering, anisotropic diffusion and watershed transformation. The final output is obtained by interpolation. This algorithm can be useful to applications in which some part of the images are corrupted due to data transmission or needs to segment objects from an image for further processing

Adaptive pre-filtering techniques for colour image analysis

One important step in the process of colour image segmentation is to reduce the errors caused by image noise and local colour inhomogeneities. This can be achieved by filtering the data with a smoothing operator that eliminates the noise and the weak textures. In this regard, the aim of this paper is to evaluate the performance of two image smoothing techniques designed for colour images, namely bilateral filtering for edge preserving smoothing and coupled forward and backward anisotropic diffusion scheme (FAB). Both techniques are non-linear and have the purpose of eliminating the image noise, reduce weak textures and artefacts and improve the coherence of colour information. A quantitative comparison between them will be evaluated and also the ability of such techniques to preserve the edge information will be investigated

A Data-Driven Edge-Preserving D-bar Method for Electrical Impedance Tomography

In Electrical Impedance Tomography (EIT), the internal conductivity of a body is recovered via current and voltage measurements taken at its surface. The reconstruction task is a highly ill-posed nonlinear inverse problem, which is very sensitive to noise, and requires the use of regularized solution methods, of which D-bar is the only proven method. The resulting EIT images have low spatial resolution due to smoothing caused by low-pass filtered regularization. In many applications, such as medical imaging, it is known \emph{a priori} that the target contains sharp features such as organ boundaries, as well as approximate ranges for realistic conductivity values. In this paper, we use this information in a new edge-preserving EIT algorithm, based on the original D-bar method coupled with a deblurring flow stopped at a minimal data discrepancy. The method makes heavy use of a novel data fidelity term based on the so-called {\em CGO sinogram}. This nonlinear data step provides superior robustness over traditional EIT data formats such as current-to-voltage matrices or Dirichlet-to-Neumann operators, for commonly used current patterns.Comment: 24 pages, 11 figure

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