961 research outputs found
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Low-resolution and signal-dependent noise distribution in positron emission
tomography (PET) images makes denoising process an inevitable step prior to
qualitative and quantitative image analysis tasks. Conventional PET denoising
methods either over-smooth small-sized structures due to resolution limitation
or make incorrect assumptions about the noise characteristics. Therefore,
clinically important quantitative information may be corrupted. To address
these challenges, we introduced a novel approach to remove signal-dependent
noise in the PET images where the noise distribution was considered as
Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation
(GAT) was used to stabilize varying nature of the PET noise. Other than noise
stabilization, it is also desirable for the noise removal filter to preserve
the boundaries of the structures while smoothing the noisy regions. Indeed, it
is important to avoid significant loss of quantitative information such as
standard uptake value (SUV)-based metrics as well as metabolic lesion volume.
To satisfy all these properties, we extended bilateral filtering method into
trilateral filtering through multiscaling and optimal Gaussianization process.
The proposed method was tested on more than 50 PET-CT images from various
patients having different cancers and achieved the superior performance
compared to the widely used denoising techniques in the literature.Comment: 8 pages, 3 figures; to appear in the Lecture Notes in Computer
Science (MICCAI 2014
A multiresolution framework for local similarity based image denoising
In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise
Video enhancement using adaptive spatio-temporal connective filter and piecewise mapping
This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises - Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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