A Simple Image Encryption Based on Binary Image Affine Transformation and Zigzag Process

In this paper, we propose a new and simple method for image encryption. It uses an external secret key of 128 bits long and an internal secret key. The novelties of the proposed encryption process are the methods used to extract an internal key to apply the zigzag process, affine transformation, and substitution-diffusion process. Initially, an original gray-scale image is converted into binary images. An internal secret key is extracted from binary images. The two keys are combined to compute the substitution-diffusion keys. The zigzag process is firstly applied on each binary image. Using an external key, every zigzag binary image is reflected or rotated and a new gray-scale image is reconstructed. The new image is divided into many nonoverlapping subblocks, and each subblock uses its own key to take out a substitution-diffusion process. We tested our algorithms on many biomedical and nonmedical images. It is seen from evaluation metrics that the proposed image encryption scheme provides good statistical and diffusion properties and can resist many kinds of attacks. It is an efficient and secure scheme for real-time encryption and transmission of biomedical images in telemedicine.

AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS

Automatic Colorization helps to hallucinate what an input gray scale image would look like when colorized. Automatic coloring makes it look and feel better than Grayscale. One of the most important technologies used in Machine learning is Deep Learning. Deep learning is nothing but to train the computer with certain algorithms which imitates the working of the human brain. Some of the areas in which it is used are medical, Industrial Automation, Electronics etc. The main objective of this project is coloring Grayscale images. We have umbrellaed the concepts of convolutional neural networks along with the use of the Opencv library in Python to construct our desired model. A user interface has also been fabricated to get personalized inputs using PIL. The user had to give details about boundaries, what colors to put, etc. Colorization requires considerable user intervention and remains a tedious, time consuming, and expensive task. So, in this paper we try to build a model to colorize the grayscale images automatically by using some modern deep learning techniques. In colorization task, the model needs to find characteristics to map grayscale images with colored ones.

Digital Rock Physics: A Geological Driven Workflow for the Segmentation of Anisotropic Ruhr Sandstone

Over the last 3 decades, Digital Rock Physics (DRP) has become a complementary part of the characterization of reservoir rocks due to the non-destructive testing character of this technique. The use of high-resolution X-ray Computed Tomography (XRCT) has become widely accepted to create a digital twin of the material under investigation. Compared to other imaging techniques, XRCT technology allows a location-dependent resolution of the individual material particles in volume. However, there are still challenges in assigning physical properties to a particular voxel within the digital twin, due to standard histogram analysis or sub-resolution features in the rock. For this reason, high-resolution image-based data from XRCT, transmitted-light microscope, Scanning Electron Microscope (SEM) as well as geological input properties like geological diagenesis, mineralogical composition, sample’s microfabrics, and estimated sample’s porosity are combined to obtain an optimal spatial segmented image of the studied Ruhr sandstone. Based on a homogeneity test, which corresponds to the evaluation of the gray-scale image histogram, the preferred scan sample sizes in terms of permeability, thermal, and effective elastic rock properties are determined. In addition, these numerically derived property predictions are compared with laboratory measurements to obtain possible upper limits for sample size, segmentation accuracy, and a geometrically calibrated digital twin of the Ruhr sandstone. The comparison corresponding gray-scale image histograms as a function of sample sizes with the corresponding advanced numerical simulations provides a unique workflow for reservoir characterization of the Ruhr sandstone.

Quantum Local Binary Pattern for Medical Edge Detection

Edge detection is one of the most important operations for extracting the different objects in medical images because it enables delimitation of the various structures present in the image. Most edge detection algorithms are based on the intensity variations in images. Edge detection is especially difficult when the images are textured, and it is essential to consider the texture in edge detection processes. In this article, the authors propose a new procedure to extract the texture from images, called the Quantum Local Binary Pattern (QuLBP). The authors introduce two applications that use QuLBP to detect edges in magnetic resonance images: a cellular automaton (CA) edge detector algorithm and a combination of the QuLBP and the Deriche-Canny algorithm for salt and pepper noise resistance. The proposed approach to extracting texture is designed for and applied to different gray scale image datasets with real and synthetic magnetic resonance imaging (MRI). The experiments demonstrate that the proposed approach produces good results in both applications, compared to classical algorithms.