24 research outputs found
A Comparison of Image Denoising Methods
The advancement of imaging devices and countless images generated everyday
pose an increasingly high demand on image denoising, which still remains a
challenging task in terms of both effectiveness and efficiency. To improve
denoising quality, numerous denoising techniques and approaches have been
proposed in the past decades, including different transforms, regularization
terms, algebraic representations and especially advanced deep neural network
(DNN) architectures. Despite their sophistication, many methods may fail to
achieve desirable results for simultaneous noise removal and fine detail
preservation. In this paper, to investigate the applicability of existing
denoising techniques, we compare a variety of denoising methods on both
synthetic and real-world datasets for different applications. We also introduce
a new dataset for benchmarking, and the evaluations are performed from four
different perspectives including quantitative metrics, visual effects, human
ratings and computational cost. Our experiments demonstrate: (i) the
effectiveness and efficiency of representative traditional denoisers for
various denoising tasks, (ii) a simple matrix-based algorithm may be able to
produce similar results compared with its tensor counterparts, and (iii) the
notable achievements of DNN models, which exhibit impressive generalization
ability and show state-of-the-art performance on various datasets. In spite of
the progress in recent years, we discuss shortcomings and possible extensions
of existing techniques. Datasets, code and results are made publicly available
and will be continuously updated at
https://github.com/ZhaomingKong/Denoising-Comparison.Comment: In this paper, we intend to collect and compare various denoising
methods to investigate their effectiveness, efficiency, applicability and
generalization ability with both synthetic and real-world experiment
A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage
An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double-l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes
Accelerating Magnetic Resonance Parametric Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)
Quantitative magnetic resonance (MR) parametric mapping is a promising
approach for characterizing intrinsic tissue-dependent information. However,
long scan time significantly hinders its widespread applications. Recently,
low-rank tensor has been employed and demonstrated good performance in
accelerating MR parametricmapping. In this study, we propose a novel method
that uses spatial patch-based and parametric group-based low rank tensors
simultaneously (SMART) to reconstruct images from highly undersampled k-space
data. The spatial patch-based low-rank tensor exploits the high local and
nonlocal redundancies and similarities between the contrast images in
parametric mapping. The parametric group based low-rank tensor, which
integrates similar exponential behavior of the image signals, is jointly used
to enforce the multidimensional low-rankness in the reconstruction process. In
vivo brain datasets were used to demonstrate the validity of the proposed
method. Experimental results have demonstrated that the proposed method
achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and
three-dimensional acquisitions, respectively, with more accurate reconstructed
images and maps than several state-of-the-art methods. Prospective
reconstruction results further demonstrate the capability of the SMART method
in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure
Hyperspectral phase imaging based on denoising in complex-valued eigensubspace
A new denoising algorithm for hyperspectral complex domain data has been
developed and studied. This algorithm is based on the complex domain
block-matching 3D filter including the 3D Wiener filtering stage. The developed
algorithm is applied and tuned to work in the singular value decomposition
(SVD) eigenspace of reduced dimension. The accuracy and quantitative advantage
of the new algorithm are demonstrated in simulation tests and in the processing
of the experimental data. It is shown that the algorithm is effective and
provides reliable results even for highly noisy data
Sparse Representation-Based Framework for Preprocessing Brain MRI
This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació