225 research outputs found
Speckle Noise Reduction in Medical Ultrasound Images
Ultrasound imaging is an incontestable vital tool for diagnosis, it provides
in non-invasive manner the internal structure of the body to detect eventually
diseases or abnormalities tissues. Unfortunately, the presence of speckle noise
in these images affects edges and fine details which limit the contrast
resolution and make diagnostic more difficult. In this paper, we propose a
denoising approach which combines logarithmic transformation and a non linear
diffusion tensor. Since speckle noise is multiplicative and nonwhite process,
the logarithmic transformation is a reasonable choice to convert
signaldependent or pure multiplicative noise to an additive one. The key idea
from using diffusion tensor is to adapt the flow diffusion towards the local
orientation by applying anisotropic diffusion along the coherent structure
direction of interesting features in the image. To illustrate the effective
performance of our algorithm, we present some experimental results on
synthetically and real echographic images
Fast Local Laplacian Filters: Theory and Applications
International audienceMulti-scale manipulations are central to image editing but they are also prone to halos. Achieving artifact-free results requires sophisticated edge- aware techniques and careful parameter tuning. These shortcomings were recently addressed by the local Laplacian filters, which can achieve a broad range of effects using standard Laplacian pyramids. However, these filters are slow to evaluate and their relationship to other approaches is unclear. In this paper, we show that they are closely related to anisotropic diffusion and to bilateral filtering. Our study also leads to a variant of the bilateral filter that produces cleaner edges while retaining its speed. Building upon this result, we describe an acceleration scheme for local Laplacian filters on gray-scale images that yields speed-ups on the order of 50×. Finally, we demonstrate how to use local Laplacian filters to alter the distribution of gradients in an image. We illustrate this property with a robust algorithm for photographic style transfer
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
A discrete graph Laplacian for signal processing
In this thesis we exploit diffusion processes on graphs to effect two fundamental problems of image processing: denoising and segmentation. We treat these two low-level vision problems on the pixel-wise level under a unified framework: a graph embedding. Using this framework opens us up to the possibilities of exploiting recently introduced algorithms from the semi-supervised machine learning literature.
We contribute two novel edge-preserving smoothing algorithms to the literature. Furthermore we apply these edge-preserving smoothing algorithms to some computational photography tasks. Many recent computational photography tasks require the decomposition of an image into a smooth base layer containing large scale intensity variations and a residual layer capturing fine details. Edge-preserving smoothing is the main computational mechanism in producing these multi-scale image representations. We, in effect, introduce a new approach to edge-preserving multi-scale image decompositions. Where as prior approaches such as the Bilateral filter and weighted-least squares methods require multiple parameters to tune the response of the filters our method only requires one. This parameter can be interpreted as a scale parameter. We demonstrate the utility of our approach by applying the method to computational photography tasks that utilise multi-scale image decompositions.
With minimal modification to these edge-preserving smoothing algorithms we show that we can extend them to produce interactive image segmentation. As a result the operations of segmentation and denoising are conducted under a unified framework. Moreover we discuss how our method is related to region based active contours. We benchmark our proposed interactive segmentation algorithms against those based upon energy-minimisation, specifically graph-cut methods. We demonstrate that we achieve competitive performance
Video Magnification for Structural Analysis Testing
The goal of this thesis is to allow a user to see minute motion of an object at different frequencies, using a computer program, to aid in vibration testing analysis without the use of complex setups of accelerometers or expensive laser vibrometers. MIT’s phase-based video motion processing was modified to enable modal determination of structures in the field using a cell phone camera. The algorithm was modified by implementing a stabilization algorithm and permitting the magnification filter to operate on multiple frequency ranges to enable visualization of the natural frequencies of structures in the field. To implement multiple frequency ranges a new function was developed to implement the magnification filter at each relevant frequency range within the original video. The stabilization algorithm would allow for a camera to be hand-held instead of requiring a tripod mount. The following methods for stabilization were tested: fixed point video stabilization and image registration. Neither method removed the global motion from the hand-held video, even after masking was implemented, which resulted in poor results. Specifically, fixed point did not remove much motion or created sharp motions and image registration introduced a pulsing effect. The best results occurred when the object being observed had contrast from the background, was the largest feature in the video frame, and the video was captured from a tripod at an appropriate angle. The final program can amplify the motion in user selected frequency bands and can be used as an aid in structural analysis testing
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