نشریه ماشین بینایی و پردازش تصویر
سال هشتم شماره 2 (تابستان 1400)

Image denoising is a well explored topic. Generally, image denoising approaches can be categorized as spatial domain and transform domain methods according to the image representation. Transform domain methods can be divided into two main groups according to their basis functions. Transform domain methods with data adaptive basis functions and transform domain methods with fixed basis functions. Fixed basis functions transform methods, in which, wavelet transform is the most popular, have been widely used for noise reduction applications due to their features and properties, such as frequency / space separation. Also, due to the non-static nature of natural images and the addition of noise to them, statistical methods have received a lot of attention among transform methods. In the present paper, after a brief introduction of denoising methods, the most important statistical models in the fixed basis transform domain are studied. The experimental results are discussed and analyzed to determine the advantages and disadvantages of these methods. The comprehensive study in this paper is a good reference for new research ideas in image denoising.

The diagnosis of cancer is mainly performed by visual analysis of pathologists through examining the morphology of the tissue slices under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and without texture. Therefore, chemical staining is required to create adequate contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, and types of illness, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for stain normalization have been proposed as a pre-processing step in the pipeline of the automated systems. The pix2pix methodwhich is derived from the conditional Generative Adversarial Networks (cGAN) is one of the powerful methods for solving image-to-image translation problems. The main innovation of this paper is to present a new powerful method for the stain normalization of histopathology images using the Pix2Pix method, which is implemented and evaluated on the Mitos-Atypia-14 dataset.In the proposed method, grayscale images are given as input to the network, and then the system learns to restain the texture of the input image in a specific coloring style by preserving the structure and corresponding histopathological pattern. This method, compared to previous methods that relied on a reference image, instead uses the distribution of all images in the learning phase. The proposed method has achieved significant resultsboth in quantitative and qualitative evaluations comparing to some well-known methods in the literature.Moreover, as another innovation, the proposed method tested in a clinical use-case, namely breast cancer tumor classification,using the PatchCamelyon datasetand itshowsa 5% increase in the AUC parameter.

An image is a visual representation of something that has been created or copied and stored in electronic form. Securing images is becoming an important concern in today’s information security due to the extensive use of images that are either transmitted over a network or stored on disks. Since public media are unreliable and vulnerable to attacks, Image encryption is the most effective way to fulfil confidentiality and protect the privacy of images over an unreliable public media.In this paper a new image encryption algorithm based on Advanced Encryption Standard and DNA sequence is proposed. We present how to encode and decode data in a DNA sequence based on Codon replacement and how to perform the different steps of AES based DNA. The algorithm is implemented in MATLAB 2012b and various performance metrics are used to evaluate its efficacy. The theoretical and experimental analysis show that the proposed algorithm is efficient in speed and precision. Furthermore, the security analysis proves that proposed algorithm has a good resistance against the noise and known attacks; So that Unbreakability of proposed algorithm is 37.48% better than the compared algorithms.