This study proposes a new fuzzy adaptive filter for the restoration of impulse corrupted digital images. The proposed filter incorporates fuzzy functions to model the uncertainties, while detecting and correcting impulses. The traditional, SMALL fuzzy function is used to identify the non-impulsive nature of the detected corrupted pixels in the initial step. For the better restoration of detected impulsive pixels, a modified version of Gaussian function is utilised to determine the similarity among the detected uncorrupted pixels. The proposed correction scheme provides more weight to the uncorrupted pixels that show much similarity with other uncorrupted pixels in the window while replacing impulses. The proposed filter adapts to various noisy and image conditions and is capable of suppressing noise while preserving image details. The experimental results in terms of subjective and objective metrics favour the proposed algorithm than many other prominent filters in literature.

Abstract:The paper presents an improved digital image watermarking algorithm by incorporating the discrete Fourier transform (DFT) and singular value decomposition (SVD). The Fourier transformed carrier image is decomposed into four different frequency subbands by the proposed onion peel decomposition (OPD) algorithm and the SVD-based watermarking scheme is applied to attach the transformed watermark in all four carrier subbands. The proposed inverse OPD algorithm together with inverse DFT is utilized to reconstruct the watermarked image from the frequency blocks.The watermark extraction algorithm is simple and it performs the inverse of watermarking process. The experimental analysis on different images shows that the proposed algorithm produces good quality watermarked images. The watermarks extracted from watermarked images inflicted with potential attacks are also of better perception quality than those produced by other prominent algorithms in terms of subjective and objective metrics.

Data is very important nowadays for almost all organizations for their existence as well as for their growth. The Internet has become the major source of data for individuals and almost all organizations. Authentic Websites are a major source of reliable data for many individuals and organizations. Extracting Data from websites is commonly referred as Web Scrapping, which refers to both manual and automated process.  Extracting large amount of meaning full data from the websites manually is very difficult, tedious and redundant task. Automated Scrapping is done by writing specific programs to extract the required data from the websites. These programs are usually called as web scrappers. Web scrappers are written using many programming languages like Python, Node.js, Ruby, C++, PHP etc. Each language has its own unique features and built in libraries for performing data extraction. There are many web scrapping tools like Beautiful Soup, Octoparse, Parsehub etc. In this article we are going to analyses few recent Web Scrappers tools used in scrapping the Web.

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A new directional filter for the restoration of random valued impulse noise is proposed by addressing the limitations of Directional Weighted Median Filter (DWMF) published by Yiqiu Dong et. al. (3). The proposed filter work better for all natural images with varied noise levels. The experimental results in terms of objective and subjective matrices show that the proposed filter work better for both images corrupted with higher or lower levels of noise while preserving the image details.