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Digital image watermarking is a technique adopted to get rid of the increasing piracies in digital images. Computerized information can be effectively duplicated, altered and falsifications be made by anybody having a PC. Most inclined to such vindictive assaults are the watermarked pictures distributed in the Internet. Advanced Watermarking can be utilized as a device for finding unapproved information reuse and furthermore for copyright security. In the existing method, texturization dependant image watermarking methodology is performed which involves the embedding and extraction of a logo image to and from an original image respectively. After finding out the texture regions of host image, the logo image is embedded into the identified texture regions by Discrete Wavelet Transform. Before embedding, according to the textual characteristics of the host image analyzed, texturization of a logo is done by using Arnold transform and a rotation. It is effective for attaining a similar texture for both logo image and host image. Later the logo image is extracted back. Discrete Wavelet Transform results in degradation of quality and robustness of watermarked image. Also it is not a difficult task for an attacker to compromise the Arnold transform and rotation performed. In this work, Lifting Wavelet Transform technique is used instead of the Discrete Wavelet Transform as it overcome the above mentioned drawback. In addition, Arnold transform and rotation is replaced with circular shift method for enhancing security.

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In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.

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