A Review and Analysis of The Existing Literature on Monochromatic Photography Colorization Using Deep Learning

It is universally known that, through the process of colorization, one aims at converting a monochrome image into one of color, usually because it was taken by the limited technology of previous decades. Our work introduces the problem, summarizes the general deep learning solutions, and discusses the experimental results obtained from open-source repositories. Although the surveyed methods can be applied to other fields, solely the content of photography is being considered. Our contribution stands in the analysis of colorization in photography by examining used datasets and methodologies for evaluation, data processing activities, and the infrastructure demanded by these systems. We curated some of the most promising papers, published between 2016 and 2021, and centered our observations around software reliability, and key advancements in solutions employing Generative Adversarial Networks and Neural Networ

METHOD OF ENCRYPTION/DECRYPTION OF DATA ON THE BASIS OF THE PIXEL ALPHABET OF MONOCHROME IMAGE

Improving the availability of information technology and increasing the volume of digital traffic leads to an important problem of data protection. A particularly pressing issue is the problem of transmitting confidential data through unsecured communication channels, such as the Internet. Recently, there has been a significant increase in the number of cyberattacks, including attempts to intercept and steal confidential information transmitted through global information networks. Information security in computer information and telecommunication systems is a priority. To date, one of the most reliable methods of protecting information is rightly considered to be encryption. Cryptographic data transformations are the most effective way for a system to maintain the confidentiality of information as it is entered, output, transmitted, processed, and stored, and to resist its destruction, theft, or distortion. But the most effective way to ensure the confidentiality of information is the combined use of steganographic and cryptographic means. In order to ensure high stability of encrypted information when transmitting it through the network of information and telecommunications systems and reduce the threat of unauthorized access to it or attack on the cipher, it is proposed to change the approach to solving the problem of data encryption. A method of encrypting / decrypting digital text information based on the pixel alphabet of a monochrome image, which is based on hiding or distorting graphic data, is proposed. This approach allows you to ensure high stability of encrypted information and significantly reduce the risk of unauthorized access to confidential information or attack on the cipher by encrypting each character with a dynamic random number from the range of values of the corresponding character and hiding the encrypted text in the position of graphic data. sender and recipient only.

Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.

Application of a wavelet transform for multispectral image fusion

A method of image fusion based on wavelet decomposition of the original images is considered. An integrated monochrome image is formed from images of the same scene obtained in different spectral ranges. A strategy for fusing detail coefficients by comparing the proportions of their magnitudes for all original images is proposed. The fusion procedure does not require any thresholds. This fusion procedure can be performed for any number of original images. The algorithm does not introduce additional distortions and collects all the necessary information from the original images. The quantitative and qualitative evaluation of the results was performed. The proposed algorithm can be used in optoelectronic systems for automatic image processing.

A simple and reproducible method for production of protein nanoparticles at biological pH using egg white

Protein nanoparticles have been found to be of great interest as a carrier in a drug delivery system due to its biodegradability and non toxic nature. The purpose of the present investigation is to establish a simple and fast method for the preparation of stable egg white protein (EWP) nanoparticles. Desolvation process was adopted and the resulting nanoparticles were stabilized by a crosslinker, glutaraldehyde. To get the suitable and stable nanoparticles several process parameters such as pH, agitation speed, concentration of egg white, rate of addition of desolvating agent, gluteraldehyde concentration and addition of salt and buffer were examined. The minimum size of 112nm has been obtained at pH 9.0 when ethanol addition rate was 1ml/min at an agitation speed of 550 rpm. The size of nanoparticles is affected largely by pH of egg white while it is not significantly affected by the agitation speed and concentration of egg white and crosslinker. The SEM monochrome image of eggwhite nanoparticles displays the spherical shape with around 100nm size.

ANALISIS TEKSTUR PHOTO LAMA MENGGUNAKAN FITUR TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIKS PADA PEWARNAAN CITRA OTOMATIS

<p> Image processing is important in a process of introduction, classification or segmentation or other processes. One thing that can be done is an analysis of the texture features related to old photos in this case grayscale photos. The object of the research can be an old photo (image) and use a statistical method based on Gray Level Counseling Matrix (GLCM). GLCM is one of the methods used for extracting texture features, some of which are analyzed using glcm by comparing the GLCM texture feature in the old photo with the original photo The coloring process is to provide more visualization of an object, it can be a monochrome image or video with the aim of providing details and clarity of the colored image or video. The study discusses grayscale images to be colored, then searches for GLCM texture feature values. The size of the features obtained from the calculation is used to find out how much the error value indirectly shows how much the image is similar. The measurement of the success of the small scale using the method of Mean Square Error (MSE) and Mean Absolute Error (MAE).</p><p>Keyword: Texture, Glcm, MAE, MSE </p>