Digital Image Processing

J. Annie Bromy Cruz ¹, Vinolia Anandan ¹

Affiliations
1 Department of Computer Science and Engineering, Anna University of Technology, Tirunelveli, IN

Abstract

The objective of this paper is to provide a novel approach for denoising the image. Image denoising is an important image processing task. Image denoising refers to the recovery of image that has been contaminated by additive white Gaussian noise. To clear the noise several denoising methods are used. The novel denoising approach used here is based on Averaging Reconstructed Images (AVREC). The reconstruction is used to reduce noise while preserving almost all useful image information. The averaging of the reconstructed images further reduces reconstruction errors and noise. This approach first divides the spectrum of the noisy image into different parts. The reconstructed image is obtained from every such partial spectrum using a 2-D singularity function analysis model. Finally, the denoising is achieved through averaging all of the reconstructed images because each of the reconstructed images is expressed as the sum of the same noise-free image and a different smaller noise image. Magnetic Resonance Images are used as the experimental images. The performance of the AVREC method is compared with the bilateral filter and median filter. Also, the novel image denoising scheme is applied for different type of noises such as Poisson noise and Speckle noise. The experimental results show that the proposed approach performs well than the bilateral filter and the median filter.

Keywords

Denoising, Partial Spectrum, Reconstruction, Singular Spectrum Analysis.

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K. Jeyageetha ¹, Vinolia Anandan ¹

Affiliations
1 Department of Computer Science and Engineering, Anna University of Technology, Tirunelveli, IN

Abstract

This paper provides a novel scheme for efficient Content-Based Image Retrieval. With the vast amount of multimedia data generated and transmitted in digital formats, content-based data management has emerged as an important area. Content-Based Image Retrieval (CBIR) systems, is used to retrieve images based on their similarity with one or more query images. This is one of the primary tools used by physicians for the comparison of previous and current medical images associated with pathologic conditions. The novel approach used is formalized according to the PAtterns for Next generation DAtabase systems (PANDA) framework for pattern representation and management. This scheme involves block-based low-level feature extraction from images followed by the clustering of the feature space to form meaningful patterns. The Wavelet Lifting Technique is used to extract the features from the medical images. An Fuzzy C Means algorithm is used to cluster the feature space. It is an iterative approach to automatically determine the number of clusters. The clusters resulting from the Fuzzy C Means algorithm are considered as patterns extracted from the image database, and are represented and handled according to the PANDA formalization. The similarity between two clusters is estimated as a function of the similarity of both their structures and the measure components. A large set of reference radiographic images are used as the experimental datasets.

Keywords

Patterns for Next Generation Database Systems, 2-D Wavelet Transform, Expectation-Maximization.

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