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There has been a rapid growth in the use of digital images due to development of modern technology. And easily available image editing tools and software's have made these images easy to distribute, manipulate, and duplicate. All these reasons are enforcing a need for copyright enforcement methods that can provide copyright to the owner. In our proposed method a PNG image is used as the cover image in which the alphachannel value of each pixel is set to default. That is, the cover image is a totally transparent color one at the beginning of the proposed data hiding process. When we apply data hiding technique to any PNG image which has transparency, it distorts the image transparency, which causes visibility issues with PNG image. To overcome this issue proposed technique uses 3 LSB of alpha channel to embed signals in image. In proposed technique owner encrypted information and hash code using SVD is embedded in image. SVD hash can be used to cross check the image contents. Experimental result show, there is no visible defect after embedding authentication signal into the cover images using proposed technique. In this paper, the proposed technique embeds the copyright information (authentication signal) which is encrypted using 128 bit AES and Singular Value Data (SVD) by using Shamir secret sharing data hiding technique. This technique is very robust and more secure as compared to previous data hiding techniques.

In this paper we proposed Independent histogram equalization (HE) method is used to enhance the hidden image detail and to increase the contrast of input image having a low dynamic range as well as preserve the mean brightness of the image. Such technique used optimal threshold method to partition the original image histogram before applying HE. This is much able to produce better result of output image than the original image by increasing the gray level difference (i.e. contrast) among object and background. The genetic algorithm determines the values of the Gaussian function parameters which further evaluates the threshold value for the partitioning of the histogram. It overcomes the drawback of HE technique because it is unable to preserve the average brightness of the image. In theory, after applying HE the average brightness shift towards the middle of the gray scale. This is the major drawback of HE.

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Image denoising is a traditional yet essential issue in low level vision. Existing denoising technique denoise image but these techniques doesn't concern about multiplicative noise removals. Due to that image texture are not preserved and PSNR value does not properly improved. Image denoising technique uses a novel Gradient Histogram Preservation (GHP) algorithm which preserves image quality. Presently, this technique denoises only additive noise removal. It cannot be applied to non-additive removal, such as multiplicative, Poisson noise and signalindependent noise and it also takes more time in calculations. Since both the noises are dissimilar in nature therefore it is difficult to eliminate both the noises by using single filter. To solve the above issue ,in this paper a novel GHP approach is used to remove additive white Gaussian noise (AWGN) effectively. Since speckle noise is multiplicative in nature; it is converted into additive noise by logarithmic transformation method before apply GHP algorithm. In this paper we use the approach that is to acquire a logarithmic transformation, calculate a covariance matrix of the transformed data, generate random number which follows mean zero and variance/covariance c times the variance/covariance computed in the previous step, then take antilog of the normalized data and apply novel technique using, Fast Fourier Transfer (FFT), Gaussian filter, local content metrics texture ,Iterative Histogram Specifications (IHS) which can denoise both types of noise removal, additive and non-additive noise removal and also takes less calculation time.. In image processing FFT is used in a wide variety of applications, like image analysis, image reconstruction, image filtering and image compression . Gaussian separating is utilized to obscure pictures and evacuate clamor. The proposed algorithm offers to remove the multiplicative noise and improves the visual quality of images.