Rajesh Siddavatam

A fast novel algorithm for salt and pepper image noise cancellation using cardinal B-splines

This paper discusses a novel algorithm for salt and pepper image noise cancelation using cardinal B-splines. The purpose of this paper is to present an analysis and application of cardinal B-splines for image noise cancellation. To apply the cardinal B-splines, one should analyze the different properties of the cardinal B-spline. Here we make use of the interpolation property and compact support of the cardinal B-splines. There are various assumptions and conditions that are considered while applying the cardinal B-splines for noise removal. The result of denoising the images affected up to 95 % of the salt and pepper noise has been shown. The results of proposed method are being compared with the other existing methods, and the comparison shows the better performance of our method.

Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets

In this paper, we propose a novel adaptive median-based lifting filter for image de-noising which has been corrupted by homogeneous salt and pepper noise. The median-based lifting filter removes the noise of the input image by calculating the median of the neighboring significant pixels. The algorithm for image noise removal uses the lifting scheme of the second-generation wavelets in conjunction with the proposed adaptive median-based lifting filter. The experimental results demonstrate the efficiency of the proposed method. The proposed algorithm is compared with all the basic filters, and it is found that our method outperforms many other algorithms and it can remove salt and pepper noise with a noise level as high as 90%. The algorithm works exceedingly well for all levels of noise, as illustrated in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) measures.

A hybrid semi-blind digital image watermarking technique using lifting wavelet transform — Singular value decomposition

The lifting wavelet transform (LWT) is a recent approach to wavelet transform and singular value decomposition (SVD) is a valuable transform technique for robust digital watermarking. While LWT allows generating an infinite number of discrete biorthogonal wavelets starting from an initial one, singular values (SV) allow us to make changes in an image without affecting the image quality much. This paper presents an approach which tries to amalgamate the features of these two transforms to achieve a hybrid and robust digital image watermarking techniques. Certain performance metrics are used to test the robustness of the method against common image processing attacks.

Robot motion planning in a dynamic environment using offset Non-Uniform Rational B-Splines (NURBS)

A new method of collision free motion path planning using Non uniform rational B- Spline curves for mobile robots is addressed in this paper. Obstacles of polygonal nature are used and the navigation occurs in a dynamic environment. Mathematical Modeling with vector addition technique is used to compute the set of points which will form the final path. NURBS technique is applied on the computed set of points which results in smooth curve as the final path, giving the advantage of more efficient motion. NURBS offsets of the computed NURBS path have been defined to avoid collision. Number of obstacles is not a constraint in the model presented. Number and position of obstacles can be changed dynamically which will be encountered by the robot and the path computed will be updated accordingly ensuring the shortest path.

A Novel Image Reconstruction Using Second Generation Wavelets

In this paper, a novel Second Generation Wavelets based lifting filter for image reconstruction has been proposed. This paper, presents a progressive image reconstruction scheme based on the multi-scale edge representation of images. In the multi-resolution edge representation an image is decomposed into Most Significant Points which represent the strong edges and Insignificant Points which represent weak edges. Image reconstruction is done based on the approximation of image using detailed coefficients obtained by using Second Generation Wavelets over adapted Delaunay triangulation. The proposed method improves the quality of the reconstructed image very efficiently as suggested by the PSNR measures.

Combined and Improved Framework of Infrastructure as a Service and Platform as a Service in Cloud Computing

Cloud computing is based on five attributes: multiplexing, massive scalability, elasticity, pay as you go, and self provisioning of resources. In this paper, we describe various cloud computing platforms, models, and propose a new combined and improved framework for Infrastructure as a Service (IAAS) and Platform as a Service (PAAS). As we know, that PAAS Framework has certain desirable characteristics that are important in developing robust, scalable, and hopefully portable applications like separation of data management from the user interface, reliance on Cloud Computing standards, an Integrated Development Environment, Life cycle management tools but it also has some drawbacks like the PAAS platform such as in Google Application engine, a large number of web servers catering to the platform are always running. This paper proposes an architecture which combines IAAS and PAAS framework and remove the drawbacks of IAAS and PAAS and describes how to simulate the cloud computing key techniques such as data storage technology (Google file system), data management technology, Big Table as well as programming model, and task scheduling framework using CLOUDSIM simulation tool.

Fast novel image reconstruction Algorithm using Adaptive R-tree based segmentation and Linear Bivariate Splines

This paper presents a fast and novel method of image reconstruction using Adaptive R-tree based segmentation and Linear Bivariate Splines. We have used our own Significant Pixel Selection Algorithm [8] to represent the strong edges which are then stored in an adaptive R-tree to enhance and improve image reconstruction. The image set can be encapsulated in a bounding box which contains the connected parts of the edges found using edge-detection techniques. Image reconstruction is done based on the approximation of image regarded as a function, by a linear spline over adapted Delaunay triangulation. The proposed method is compared with some of the existing image reconstructions.

Geometric Robust Multimodal Biometric Watermarking Scheme for Copyright Protection of Digital Images

paper proposes a copyright protection system using multimodal biometrics for content protection of digital images. Two biometric traits, one physiological (face) and one behavioral (offline handwritten signature) of the legitimate owner are used as watermarks. These watermarks are embedded at different levels of resolution to the high frequency wavelet coefficients through optimal control of the embedding factor. Experimental evaluation confirms the imperceptibility and robustness of the scheme against a family of typical image distortions functions like Jpeg Compression, filtering etc as well as geometric attacks such as scaling, rotation and combined attacks.

A Novel Biometric Watermaking Approach Using LWT- SVD

The lifting wavelet transform (LWT) is a recent approach to wavelet transform, and singular value decomposition (SVD) is a valuable transform technique for robust digital watermarking. While LWT allows generating an infinite number of discrete biorthogonal wavelets starting from an initial one, singular values (SV) allow us to make changes in an image without affecting the image quality much. This paper presents an approach which tries to amalgamate the features of these two transforms to achieve a hybrid and robust digital image watermarking techniques. Certain performance metrics are used to test the robustness of the method against common image processing attacks.

A Fast Progressive Image Transmission Algorithm Using Linear Bivariate Splines

Progressive image transmission provides a convenient User Interface when images are transmitted slowly. In this paper, we present a progressive image reconstruction scheme based on the multi-scale edge representation of images. In the multi-scale edge representation an image is decomposed into Most Significant Points which represent the strong edges and Insignificant Points which represent weak edges. Image re-construction is done based on the approximation of image regarded as a function, by a linear spline over adapted Delaunay triangulation. The proposed method progressively improves the quality of the reconstructed image till the desired quality is obtained.