Like the digital watermark, which has been highlighted in previous studies, the quantum watermark aims to protect the copyright of any image and to validate its ownership using visible or invisible logos embedded in the cover image. In this paper, we propose a method to include an image logo in a cover image based on quantum fields, where a certain amount of texture is encapsulated to encode the logo image before it is included in the cover image. The method also involves transforming wavelets such as Haar base transformation and geometric transformation. These combination methods achieve a high degree of security and robustness for watermarking technology. The digital results obtained from the experiment show that the values of Peak Sig

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

Abstract

 The objective of image fusion is to merge multiple sources of images together in such a way that the final representation contains higher amount of useful information than any input one.. In this paper, a weighted average fusion method is proposed. It depends on using weights that are extracted from source images using counterlet transform. The extraction method is done by making the approximated transformed coefficients equal to zero, then taking the inverse counterlet transform to get the details of the images to be fused. The performance of the proposed algorithm has been verified on several grey scale and color test  images, and compared with some present methods.

Medical imaging is a technique that has been used for diagnosis and treatment of a large number of diseases. Therefore it has become necessary to conduct a good image processing to extract the finest desired result and information. In this study, genetic algorithm (GA)-based clustering technique (K-means and Fuzzy C Means (FCM)) were used to segment thyroid Computed Tomography (CT) images to an extraction thyroid tumor. Traditional GA, K-means and FCM algorithms were applied separately on the original images and on the enhanced image with Anisotropic Diffusion Filter (ADF). The resulting cluster centers from K-means and FCM were used as the initial population in GA for the implementation of GAK-Mean and GAFCM. Jaccard index was used to s

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

     Image content verification is to confirm the validity of the images, i.e. . To test if the image has experienced any alteration since it was made. Computerized watermarking has turned into a promising procedure for image content verification in light of its exceptional execution and capacity of altering identification.

     In this study, a new scheme for image verification reliant on two dimensional chaotic maps and Discrete Wavelet Transform (DWT) is introduced. Arnold transforms is first applied to Host image (H) for scrambling as a pretreatment stage, then the scrambled host image is partitioned into sub-blocks of size 2×2 in which a 2D DWT is utilized on  ea

     This paper presents a new RGB image encryption scheme using multi chaotic maps. Encrypting an image is performed via chaotic maps to confirm the properties of secure cipher namely confusion and diffusion are satisfied. Also, the key sequence for encrypting an image is generated using a combination of   1D logistic and Sine chaotic maps. Experimental results and the compassion results indicate that the suggested scheme provides high security against several types of attack, large secret keyspace and highly sensitive.   

       In recent years, encryption technology has been developed rapidly and many image encryption methods have been put forward. The chaos-based image encryption technique is a modern encryption system for images. To encrypt images, it uses random sequence chaos, which is an efficient way to solve the intractable problem of simple and highly protected image encryption. There are, however, some shortcomings in the technique of chaos-based image encryption, such limited accuracy issue. The approach focused on the chaotic system in this paper is to construct a dynamic IP permutation and S-Box substitution by following steps. First of all, use of a new IP table for more diffusion of al