OCT image Denoising Based On K-SVD Method

GRD Journals | Global Research and Development Journal for Engineering | International Conference on Innovations in Engineering and Technology (ICIET) - 2016 | July 2016

e-ISSN: 2455-5703

OCT image de noising Based On K-SVD Method 1A.Priyanga 2V.Nandhini

Devi 3M.Tamilarasi 4Dr.S.Venkatanarayanan 1,2 UG Student 3Assistant Professor 4Associate Professor 1,2,3 Department of Electronics and Communication Engineering 4Department of Electrical and Electronics Engineering 1,2,3 SACS MAVMM Engineering College, Madurai, Tamilnadu, India 4K .L. N. Engineering College of Engineering, Pottapalayam, Tamilnadu, India Abstract Enhancement of the images will be more helpful in surveillances and remote sensing applications. While increasing the size of the image the original image quality will be affected. In order to avoid the loss of quality while enhancing the image, Non Local Means (NLM) Optimized Sparse Method is used. The noises and the pixel differences occurring in the up sampling and down sampling of the images were identified and they were removed based on the proposed method. Finally the images were enhanced by the edge enhancement process. The performance of the proposed method is proved using the performance parameters. The input OCT images were taken as input. Noises were added to the input images producing a noisy images. The input images were denoised based on the K-SVD process. K-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. It aims to partition n observations into few clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The denoised images were then clustered and edges of the clusters were identified. The clusters were estimated based on the FCM clustering process. The performance of the process is then measured using the performance parameters like PSNR, MSE and SSIM. Keyword- DE noising, Optical Coherent Tomography, Dual Tree, K-SV, Clustering, Prototype __________________________________________________________________________________________________

I. INTRODUCTION TSparse representation-based super-resolution (SR) techniques are extensively studied in recent years. They attempt to capture the co-occurrence prior between low-resolution (LR) and high-resolution (HR) image patches. Yang et al used a coupled dictionary learning model for image super-resolution. They assumed that there exist coupled dictionaries of HR and LR images, which have the same sparse representation for each pair of HR and LR patches. After learning the coupled dictionary pair, the HR patch is reconstructed on HR dictionary with sparse coefficients coded by LR image patch over the LR dictionary. In this typical framework of sparse representation-based SR method, the dictionary is determined on a general training set and the prior model to constrain the restoration problem is the sparsely of each local patch. Many dictionary learning methods aim at learning a universal dictionary on a general training set to represent various image structures. However, for complex natural images, sparse decomposition over a highly redundant dictionary is potentially unstable and tends to generate visual artefacts. In other words, universal dictionaries are not adaptive to local image properties. Therefore, it is reasonable to improve the dictionary learning model for more adaptive dictionaries. Fortunately, the rapid development of social network provides us with large amount of similar images describing the same scene. This means similar images of the LR image can be gathered to train an adaptive dictionary. Moreover, inspired by the work, we consider introducing the saliency property of images to further improve the adaptiveness of the dictionary. Saliency refers to elements of a visual scene that are likely to attract the attention of human observers. More generally, regions salient to human eyes tend to be highly structured because human visual system is attracted to organized structures for the ease of recognition. Sadakaet al also suggested that due to human visual attention, attended regions are processed at high visual acuity, hence details in these regions should be reconstructed with higher accuracy than those in nonattended areas. Thus when training dictionaries, we specially use samples from salient regions. The fact that salient regions of similar images probably have similar structures would enhance the adaptiveness and reconstruction ability of dictionaries. When reconstructing the image, the above trained dictionary can be applied to the salient regions in the LR image to generate more visually pleasant results while reducing the overall computation cost. As to the prior model to recover the HR image, conventional sparse recovery algorithms imposed the sparsely constraint of each independent patches. The local smooth-ness is constrained merely by averaging on overlapped regions, which is weak to regularize the image SR problem when the observed LR image loses partial structure information.

Correlations between the structural information of the whole patches (not merely the overlapped regions) should be investigated. Thus context-aware sparse decomposition is introduced, which refers to sparsely coding the patches by employing All rights reserved by www.grdjournals.com

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OCT image de noising Based On K-SVD Method (GRDJE / CONFERENCE / ICIET - 2016 / 089)

the dependencies between the dictionaries atoms used to decompose the patches. Better still, the highly structured property of salient regions makes it a fairly proper scenario to apply context-aware sparse coding. In this paper, considering the aforementioned two issues, we present a novel saliency-modulated context-aware sparse decomposition method for image super resolution. Similar images are obtained from the Internet by content-based image retrieval to build a specialized database. Then example patches from the salient regions of the database are extracted to train a salient dictionary, which is especially adaptive to local structures. In addition, to better explore the correlations among patches, we apply context-aware sparse decomposition to salient regions based on the observation that salient regions tend to be more structured.

This paper addresses the problem of generating a super-resolution (SR) image from a single low-resolution input image.

We approach this problem from the perspective of compressed sensing. The low-resolution image is viewed as down sampled version of a high-resolution image, whose patches are assumed to have a sparse representation with respect to an over-complete dictionary of prototype signal-atoms. The principle of compressed sensing ensures that under mild conditions, the sparse representation can be correctly recovered from the down sampled signal. We will demonstrate the effectiveness of sparsely as a prior for regularizing the otherwise ill-posed super-resolution problem. We further show that a small set of randomly chosen raw patches from training images of similar statistical nature to the input image generally serve as a good dictionary, in the sense that the computed representation is sparse and the recovered high-resolution image is competitive or even superior in quality to images produced by other SR methods. Conventional approaches to generating a super-resolution (SR) image require multiple low-resolution images of the same scene, typically aligned with sub-pixel accuracy. The SR task is cast as the inverse problem of recovering the original high-resolution image by fusing the low-resolution images, based on assumptions or prior knowledge about the generation model from the high-resolution image to the low-resolution images. The basic reconstruction constraint is that applying the image formation model to the recovered image should produce the same low-resolution images. However, because much information is lost in the high-to-low generation process, the reconstruction problem is severely underdetermined, and the solution is not unique. Various methods have been proposed to further regularize the problem. For instance, one can choose a MAP (maximum a-posteriori) solution under generic image priors such as Huber MRF (Markov Random Field) and Bilateral Total Variation. However, the performance of these reconstruction-based super-resolution algorithms degrades rapidly if the magnification factor is large or if there are not enough low- resolution images to constrain the solution, as in the extreme case of only a single low-resolution input image. Another class of super-resolution methods that can over-come this difficulty are learning based approaches, which use a learned co-occurrence prior to predict the correspondence between low-resolution and high-resolution image patches.

The rapid decrease in the dimensions of integrated circuits with a simultaneous increase in component density have introduced resolution challenges for optical failure analysis techniques. Although optical microscopy efforts continue to increase resolution of optical systems through hardware modifications, signal processing methods are essential to complement these efforts to meet the resolution requirements for the Nan scale integrated circuit technologies. In this work, we focus on laser voltage imaging as the optical failure analysis technique and show how an over complete dictionary-based sparse representation can improve resolution and localization accuracy. We describe a reconstruction approach based on this sparse representation and validate its performance on simulated data. We achieve an 80% reduction of the localization error.

II. COMPLEX WAVELET BASED K-SVD A. OCT de noising Similar to ultrasound images, OCT suffers from speckle noise which causes erroneous interpretation. Multidimensional filtering and noise removal as the most widely used canonical filtering operation is a fundamental operation in image ,video processing , and low-level computer vision .In older OCT devices , named time-domain techniques , interferometry fringe were generated in the detector by axial movement of reference reflector. This translation could reduce the image acquisition speed significantly. In newer generation of OCT, named Fourier domain OCT, the reference reflector remains fixed, and the difference between optical path length of the sample and the reference is transformed to frequency domain. Consequently, Fourier domain OCT can reach higher speeds up to several hundred thousand lines per second. Noise reduction in OCT may be used on OCT signal before producing magnitude of OCT interference signal (complex domain or hardware based) or on magnitude of the OCT signal (magnitude domain or software based).Complex domain methods can be split into “modification in optical setup�.

1) K-SVD PROCESS K-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data. Initial dictionary is created based on the original and the noised patches of the images. The clustering method is based on the identification of the difference between the input data and based on the difference the clusters were assigned. By comparing the dictionary patches and the original patches the noises in the images were estimated. The estimated noises from the images were removed resulting in the denoised OCT images.

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OCT image de noising Based On K-SVD Method (GRDJE / CONFERENCE / ICIET - 2016 / 089)

B. Fuzzy cluster means algorithm The regions in the images that are having similar intensity is clustered using the Fuzzy C Means algorithm. In fuzzy clustering, every point has a degree of belonging to clusters. An overview and comparison of different fuzzy clustering algorithms is available. Any point in the image has a set of coefficients giving the degree of being in the kth cluster. With fuzzy c-means, the centroid of a cluster is the mean of all points, weighted by their degree of belonging to the cluster. The cluster centres were obtained and in each iteration the distance between the centre point and the image pixels were calculated and they were updated.

The process is repeated iteratively until all the pixels were grouped.

C. Dictionary formation Noise is added to the input OCT images. In the dictionary formation step the artefacts (noises) in the images were identified. The sampled images were divided into 8x8 patches. Optimization problems were then employed in the process. The constrained optimization is defined as follows, here is the coefficient vector and is the regularized parameter used for the identification of the noises in the images. Using constrained optimization process the relationship between the original pixel information’s and the magnification rate can be identified.

D. Clustering The regions in the images that are having similar intensity is clustered using the Fuzzy C Means algorithm. Any point in the image has a set of coefficients giving the degree of being in the kth cluster. With fuzzy c-means, the centroid of a cluster is the mean of all points, weighted by their degree of belonging to the cluster. Each clustered region represents regions of different intensity.

E. Edge identification The clustered images consists of different regions in the images. The regions in the images were separated and edge regions for each cluster is identified. The edge regions gives the outline of the image regions in the OCT images. The edges regions were identified based on the gradient identification of the clustered regions. The result of the process is the denoised OCT images with clearly defined edge regions in the images.

III. SIMULATION RESULT

Fig. 1: The normal OCT input image

Fig. 2: The noises are added to the input normal image

Fig. 3: Denoised image

The de noised images are the noises are removed from the noisy OCT images.

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OCT image de noising Based On K-SVD Method (GRDJE / CONFERENCE / ICIET - 2016 / 089)

Fig. 4: Initial Dictionary Formation

Initial dictionary formation are customized B-scanning patter. It’s formed by the initial dictionary formation.

Fig. 5: k-SVD Dictionary Formation

.

In the dictionary formation step the artefacts (noises) in the images were identified. The sampled images were divided into 8x8 patches. Optimization problems were then employed.

Fig .6: Clustered Images

The regions in the images that are having similar intensity is clustered using the Fuzzy C Means algorithm. Any point in the image has a set of coefficients giving the degree of being in the kth cluster.

Fig. 7: Clustered image

With fuzzy c-means, the centroid of a cluster is the mean of all points, weighted by their degree of belonging to the cluster.

The cluster centres were obtained and in each iteration the distance between the centre point and the image pixels were calculated and they were updated. The process is repeated iteratively until all the pixels were grouped.

A. Different Region

Fig. 8: Different Region

Each clustered region represents regions of different intensity. The clustered images consists of different regions in the images.

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OCT image de noising Based On K-SVD Method (GRDJE / CONFERENCE / ICIET - 2016 / 089)

B.

Cluster Regions

Fig. 9: Cluster Regions

The regions in the images were separated and edge regions for each cluster is identified.

Fig. 10: Cluster Edges

The edge regions gives the outline of the image regions in the OCT images. The edges regions were identified based on the gradient identification of the clustered regions. The result of the process is the denoised OCT images with clearly defined edge regions in the images.

C. Performance Measure

Table.1: Performance Measure

The performance of the enhancement process is measured based on the PSNR and SSIM calculation. The calculated performance metrics indicates that the proposed method is more efficient compared to the existing methods. The PSNR, TP, EP and ENL values were calculated.

IV. CONCLUSIONS The process denoising of the images were proposed. Noise is added to the input OCT images. The noisy images were then denoised based on K-SVD process. The K-SVD process forms a dictionary for the images based on the original pixel information. The formed dictionary is then compared with noisy image pixel with which the images can be denoised. The resulting denoised images were then clustered. The clustering process is then optimized based on the edge detection process which is based on the gradient information’s. The performance of the process is measured based on the performance parameters.

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