Digital Image Processing

S. Lakshmanan ¹, V. Kavitha ²

Affiliations
1 Department of Electronics and Communication, University College of Engineering, Nagercoil, IN
2 Department of CSE, Anna University, Tirunelveli, IN

Abstract

This paper presents three steps fuzzy filters for the removal of both Impulse noise and Gaussian noise in color video sequences and image sequences performances is compared with other noise removal filters parameters such as MSE, PSNR etc. There are many causes of noise in a digital camera, most often leakage currents on the sensor at high ISO, shutter speed and aperture using lens etc. We propose a novel spatio temporal fuzzy based algorithm for noise filtering of Image based on a trapezoidal membership functions and also used in fuzzy set rules. Membership functions detects noisy pixel part and fuzzy set rules detects and removal of random impulse noise removal experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.

Keywords

Additivenoise, Fuzzy Filter, Linear Filters, Mean Filters, Salt and Pepper Noise.

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Z. Faizal Khan ¹, V. Kavitha ²

Affiliations
1 Anna University of Technology, Tirunelveli, IN
2 University College of Engineering, Nagercoil Campus, Anna University of Technology, Tirunelveli, IN

Abstract

Computerized Detection of Lung cancer has been widely used for the last two decades. Image processing techniques provide a good tool for improving the manual screening of CT samples of lung. Processing of pulmonary X-ray computed tomography (CT) images is a predecessor to most of the pulmonary image analysis applications such as cancer and TB detection. The automated extraction of the lung cancer in CT images is the most crucial step in a computer-aided diagnosis (CAD) system. The CAD system is equipped with functions that automatically detect the suspicious regions from chest CT images from the Region of Interest. Significant advancements have been made in this area for the last few years. Automating the analysis of such CT data is a necessary task. This automation has created a rapidly developing research area in the field of medical imaging. This paper presents a state of art survey of various image processing methods and techniques for computerized detection of lung cancer in CT images. This paper focuses on various CAD methods for lung cancer detection such as the Lung Segmentation, Segmentation of Airways, vessels and the Segmentation of the Lobes and Fissures. In addition, research directions and future challenges focusing towards a better CAD scheme was also discussed.

Keywords

Computer-Aided Diagnosis, Lung Cancer, Lung Lobes, Fissures, Pulmonary Embolism, Image Segmentation.

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R. Gracy Star ¹, V. Kavitha ²

Affiliations
1 Moderator Gnanadason Polytechnic College, Nagercoil, Tamilnadu, IN
2 University College of Engineering, Nagercoil, IN

Abstract

Multiple Description (MD) video coding can be used to reduce the negative effects caused by transmission over error-prone networks, Various approaches have been proposed for MD coding, each having their own advantages and disadvantages. Normally any approach having higher compression ratio has lower signal-to-noise ratio. The hybrid MD coding method segments the video in both the spatial domain and frequency domain. The hybrid MD decoder takes advantage of the residual-pixel correlations in the spatial domain, and the coefficient correlations in the frequency domain, for error concealment in the case of data loss. The proposed Context Based Combined MD coding segments the image into foreground and background. The foreground which contains significant information is transmitted using four channels whereas the background is transmitted using two channels. As a result, the desired quality and better error resilience can be achieved at the foreground and faster transmission at the background, effectively reducing the total transmission time.

Keywords

Multiple Description Coding, Polyphase Sub Sampling, Spatial Segmentation, Frequency Segmentation.

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K. Karthika ¹, C. Akila ¹, V. Kavitha ²

Affiliations
1 Anna University of Technology, Tirunelveli, IN
2 Department of CSE, Anna University, Tirunelveli, IN

Abstract

Image restoration is the process of recovering high quality original image from the degraded version of the image. The noises in the digital images are introduced during their acquisition and transmission. The objective of this paper is to denoise the color images which are affected by the Gaussian and Impulse noise. In this paper, the fuzzy peer cluster concept is used. A fuzzy peer cluster will be defined as a group of pixels which are similar to the processing pixel. The fuzzy peer cluster for each image pixel will be determined and the Fuzzy rule is used to detect the impulse noise in each image pixel. Impulse noise in the image pixel is removed by using the Swapping Bilateral Filter. Gaussian noise in the pixel is detected by means of the suggested median value. Gaussian noise in the image is reduced by using the same Swapping Bilateral Filter.

Keywords

Fuzzy Technique, Image Noise, Image Restoration, Suggested Median, Swapping Bilateral Filter.

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S. Karpagalakshmi ¹, C. Akila ¹, V. Kavitha ¹

Affiliations
1 Department of CSE, Anna University of Technology, Tirunelveli, IN

Abstract

This Paper presents an appealing loom to Non Local Means (NLM) image denoising algorithm that uses Principal Component Analysis (PCA) for dimensionality reduction. This Principal Component Analysis is applied individually for the three bands namely red, green, blue of the color images crooked with a frequently occurring noise model namely the Gaussian noise. As a result of PCA, obtain the Eigen value measures on the observed data and then develop a small number of artificial data set (principal components) using Parallel Analysis that will account for most of the variance in the observed data set. The principal components can then be used as predictor or criterion variables in subsequent analysis. Consequently measure the neighborhood similarity weight for the subspace using the normalization technique. The ensuing algorithm is referred to as the Principal Neighborhood Dictionary (PND) Nonlocal Means. The performance and the computational time of the proposed method is improved by the retrieval of the significant principal components from the projected subspace for each bands and increase of subspace window size respectively. The accuracy of the proposed PND method is compared with the other state of art methods and the superior performance of the proposed image denoising method is stated in terms of the increased Peak Signal to Noise Ratio (PSNR).

Keywords

Nonlocal Means (NLM), Parallel Analysis, Principal Component Analysis, Principal Neighborhood Dictionary.

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