An Efficient Dictionary Learning Algorithm and Its Application to 3-D Medical Image Denoising (original) (raw)

An Overview on Dictionary and Sparse Representation in Image Denoising

Abstract: The goal of natural image denoising is to estimate a clean version of a given noisy image, utilizing prior knowledge on the statistics of natural images. Noise removal from natural images is a challenging task. Image denoising is an applicable issue for image processing and computer vision problems. There are several existing methods are available for image denoising. A most applicable and expected property of an image denoising is that it should totally remove the noise as well as its preserve edges. This paper represents the review of parameter and algorithms available for image denoising. Index Terms: Image noise, sparse, over-complete dictionary, Redundancy parameters

Image Denoising Metric Parameters Improvement Using Dictionary Learning and Sparse Coding20190619 112173 706j7y

IJRTE, 2019

Digital image processing uses efficient computer algorithms for image denoising and to improve the image quality. Noisy image is produced due to various reasons in image acquisition, compression, preprocessing, segmentation etc. Over the last decade, various methods have shown promising results in removing zero mean Gaussian noise from images. Apart from different strategies implemented for noise reduction; this paper proposes a method for reducing noise and to improve metric parameters. Without using pre-chosen set of basis functions to represent the image, this paper discuss about performing image denoising using dictionary learning and sparse representation. Instead of removing coefficients of noise, shrinking sparse coefficients of noise is implemented to eliminate noise and it retains the image quality

Image Denoising via Improved Dictionary Learning with Global Structure and Local Similarity Preservations

Symmetry, 2018

We proposed a new efficient image denoising scheme, which leads to four important contributions. The first is to integrate both reconstruction and learning based approaches into a single model so that we are able to benefit advantages from both approaches simultaneously. The second is to handle both multiplicative and additive noise removal problems. The third is that the proposed approach introduces a sparse term to reduce non-Gaussian outliers from multiplicative noise and uses a Laplacian Schatten norm to capture the global structure information. In addition, the image is represented by preserving the intrinsic local similarity via a sparse coding method, which allows our model to incorporate both global and local information from the image. Finally, we propose a new method that combines Method of Optimal Directions (MOD) with Approximate K-SVD (AK-SVD) for dictionary learning. Extensive experimental results show that the proposed scheme is competitive against some of the state-of-the-art denoising algorithms.

Computed Tomography image denoising utilizing an efficient sparse coding algorithm

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012

In this paper, the problem of reducing noise from low-dose Computed Tomography (CT) is investigated. The process is composed of: sparse coding, dictionary update and denoising; that is a time consuming process. Hence, despite the promising results reported in literature, it has not attracted much attention in medical applications. In an attempt to reduce the complexity and time consumed, we propose an efficient method for sparse coding approximation. In the proposed sparse coding approach, unlike most current methods the global search is performed only once. The potential representative atoms are identified and buffered, then only a local recursive pursuit within a few atoms is executed to find the sparse representation. Moreover, the K-SVD dictionary update method and its extension to image denoising is utilized for reducing the noise in CT scans. Our results demonstrate this approach is reliable and improves the accuracy and process time significantly, making the proposed method a suitable candidate for clinical purposes.

An Efficient Dictionary Learning Algorithm for Sparse Representation

2010 Chinese Conference on Pattern Recognition (CCPR), 2010

Sparse and redundant representation of data assumes an ability to describe signals as linear combinations of a few atoms from a dictionary. If the model of the signal is unknown, the dictionary can be learned from a set of training signals. Like the K-SVD, many of the practical dictionary learning algorithms are composed of two main parts: sparse-coding and dictionary-update. This paper first proposes a Stagewise least angle regression (St-LARS) method for performing the sparse-coding operation. The St-LARS applies a hard-thresholding strategy into the original least angle regression (LARS) algorithm, which enables it to select many atoms at each iteration and thus results in fast solutions while still provides good results. Then, a dictionary update method named approximated singular value decomposition (ASVD) is used on the dictionary update stage. It is a quick approximation of the exact SVD computation and can reduce the complexity of it. Experiments on both synthetic data and 3-D image denoising demonstrate the advantages of the proposed algorithm over other dictionary learning methods not only in terms of better trained dictionary but also in terms of computation time.

A scheme for X-ray medical image denoising using sparse representations

13th IEEE International Conference on BioInformatics and BioEngineering, 2013

This paper addresses the problem of noise removal in X-ray medical images. A novel scheme for image denoising is proposed, by leveraging recent advances in sparse and redundant representations. The noisy X-ray image is decomposed, with respect to an overcomplete dictionary which is either fixed or trained on the noisy image, and it is reconstructed using greedy techniques. The new scheme has been tested with both artificial and real X-ray images and it turns out that it may offer superior denoising results as compared to other existing methods.

Efficient Dictionary Learning with Sparseness-Enforcing Projections

International Journal of Computer Vision, 2015

Learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks. This paper studies the optimization of dictionaries on image data where the representation is enforced to be explicitly sparse with respect to a smooth, normalized sparseness measure. This involves the computation of Euclidean projections onto level sets of the sparseness measure. While previous algorithms for this optimization problem had at least quasi-linear time complexity, here the first algorithm with linear time complexity and constant space complexity is proposed. The key for this is the mathematically rigorous derivation of a characterization of the projection's result based on a soft-shrinkage function. This theory is applied in an original algorithm called Easy Dictionary Learning (EZDL), which learns dictionaries with a simple and fast-to-compute Hebbian-like learning rule. The new algorithm is efficient, expressive and particularly simple to implement. It is demonstrated that despite its simplicity, the proposed learning algorithm is able to generate a rich variety of dictionaries, in particular a topographic organization of atoms or separable atoms. Further, the dictionaries are as expressive as those of benchmark learning algorithms in terms of the reproduction quality on entire images, and result in an equivalent denoising performance. EZDL learns approximately 30 % faster than the already very efficient Online Dictionary Learning algorithm, and is Communicated by Julien Mairal, Francis Bach, Michael Elad.

Supervised Dictionary Learning and Sparse Representation-A Review

Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-ofthe-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solving problems such as denoising, inpainting, and coding, it may not lead to optimal solution in classification tasks, where the ultimate goal is to make the learned dictio- * Corresponding author Email addresses: mehrdad.gangeh@utoronto.ca (Mehrdad J. Gangeh), afarahat@pami.uwaterloo.ca (Ahmed K. Farahat), aghodsib@uwaterloo.ca (Ali Ghodsi), mkamel@pami.uwaterloo.ca (Mohamed S. Kamel)

Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing, 2000

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.