X-rays with photon having wavelength below 0.2-0.1 nm are generally used to produce X-ray images due to their high penetration ability. But photon counting statistics follows Poisson noise distribution which degrades quality of medical data (X-ray)represented by photon images. This Poisson noise can be reduced by increasing the dose of X-ray for production of X-ray image but it will harm patient body. In this paper, modified Harris operator along with wavelet domain thresholding is proposed for X-ray image denoising. Harris operator finds the pixels in image having more intensity variation compare to neighboring pixels, such pixels can be called as noisy pixels. We compare our denoised results with other denoising techniques and found significant improvement in result. IndexTerms-X-ray image, Poisson noise, waveletThresholding, Harris operator.

in this digital world due to rapid growth in image processing technology and internet, Piracy of the images is becoming more and more serious problem. In order to prohibit such piracy, watermarking is widely used approach. In conventional watermarking, watermark is inserted in host image by modifying its original information. This approach creates a trade-off between robustness and imperceptibility. To overcome this, zero watermarking is used. In this process instead of embedding watermark, it is created using host image and original watermark of ownership identification. Zero Watermarking does not alter original information of the image and provides perfect imperceptibility. In this paper we are proposing a robust and dynamic zero watermarking using Hessian Laplace Detector and Logistic map. Here, Feature points of host image are detected using Hessian Laplace detector and used along with original watermark of ownership identity for constructing zero-watermark. Finally constructed zero-watermark is scrambled using Logistic map to improve its security before storing it into database. Our dynamic approach lets original watermark size to be solely decided by total number of detected feature points and all pixels of original watermark to be used for creating zero-watermark. We have compared our algorithm with previous work and got better reconstruction of original watermark under noise, filtering, compression, translation and cropping attacks.

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