Applying machine learning to improve a texture type image

Abstract

The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are considered. The first step in image processing is image enhancement. The issues of signal image processing remain in the focus of attention of various specialists. Currently, along with the development of information technology, the automatic improvement of images used in any field of science is one of the urgent problems. Images were analyzed as objects: state license plates of cars, faces, sections of the field on satellite images. In this work, we propose to use the models of Super-Resolution Generative Adversarial Network (SRGAN), Extended Super-Resolution Generative Adversarial Networks (ERSGAN). For this, an experiment was conducted, the purpose of which was to retrain the trained ESRGAN model with three different architectures of the convolutional neural network, i.e. VGG19, MobileNet2V, ResNet152V2 to add perceptual loss (by pixels), also add more sharpness to the prediction of the test image, and compare the performance of each retrained model. As a result of the study, the use of convolutional neural networks to improve the image showed high accuracy, that is, on average ESRGAN+MobileNETV2 – 91 %, ESRGAN+VGG19 – 86 %, ESRGAN+ResNet152V2 – 96 %.

Noisy image enhancements using deep learning techniques

“…After classification into road image subclasses, the AttentiveGAN submodel [19] is passed through, which detects and cleans images of noise such as snow marks, raindrops, and fog. Image classification [20] Other medical and space imaging classes go through the super-resolution generative adversarial network (SRGAN) submodel [21]- [23]. It performs image enhancement using generative adversarial networks (GANs).…”

Data generation using generative adversarial networks to increase data volume

“…This paper focuses on two important methods within GANs: pixel-to-pixel (Pix2Pix) [6], [7] and regular GAN [8]- [10]. Pix2Pix, which is based on the idea of learning from paired data before and after transformation, is a powerful tool for creating high-quality images [11]- [13]. In this study, we analyze the dynamics of the losses of the generator and discriminator during the training process, the features of convergence, and the stability of this method.…”

Human-machine interactions based on hand gesture recognition using deep learning methods

“…Tussupov et al [16] presents a unique learning-based approach to denoising without the use of pure data. The authors have shown that deep neural networks can be trained with a pair of noisy images without the need for pure training data.…”