Image Share

Abstract: Visual Cryptography is an encryption technique used to hide visual information in such a way that it can be decrypted by the human visual system, without using any decryption algorithm. There exist various schemes like digital watermarking algorithm etc. In this paper we have proposed a new algorithm to enhance the security in visual cryptography. To achieve this level of security, we have proposed a Cover Image Share Embedded security algorithm (CISEA) to produce the meaningful shares from the secret image. In this algorithm we have applied a new concept for generation of compliment images of a cover image over which the shares of secret image are to be embedded. CISEA provides one more layer of security for the images in communication channel. It is shown that CISEA provides better security compared to security provided by visual cryptography using digital watermarking system and the results are tested using MS visual studio dot net platform.

Abstract: The invention relates to a joint sparse representation and deep learning-based image super resolution method and a joint sparse representation and deep learning-based image super resolution system. The method includes the following steps that: resolution reduction is performed on an original high-resolution image, so that a low-resolution image of which the size is the same as the original high-resolution image, and the difference value part of the original high-resolution image and the low-resolution image is obtained; a low-resolution image dictionary, a difference value image dictionary and corresponding sparse representation coefficients are obtained; a deep learning network with root-mean-square error adopted as a cost function is constructed, and network parameters are optimized iteratively, so that the cost function can be minimum, a trained deep learning network can be obtained; and the sparse coefficient of the low-resolution image, which is adopted as a test part, is inputted into the deep learning network, when error is smaller than a given threshold value, a corresponding high-resolution image can be reconstructed according to the low-resolution image of which the resolution is to be improved. According to the method and system of the invention, defects of an existing method according to which a joint dictionary training mode is utilized to make a high-resolution image and a low-resolution image share a sparse representation coefficient can be eliminated, and deep learning is utilized to fully learn the mapping relationship between the low-resolution image sparse representation coefficient and the difference value image sparse representation coefficient, and therefore, a high-resolution reconstruction result with higher precision can be obtained.

Abstract: This paper tries to exploit the joint group intrinsics in face recognition problem by using sparse representation with multiple features We claim that different feature vectors of one test face image share the same sparsity pattern at the higher group level, but not necessarily at the lower (inside the group) level This means that they share the same active groups, but not necessarily the same active set To this end, a hierarchical orthogonal matching pursuit algorithm is developed The basic idea of this approach is straightforward: At each iteration step, we first select the best group which is shared by different features, then we select the best atoms (within this group) for each feature This algorithm is very efficient and shows good performance in standard face recognition dataset