title: Ð¡ÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¸ÑÑÐ¾Ð²ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð² Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ðµ LAB
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Analysis and implementation of digital image colorization methods in LAB space
Creators: name: ÐÐ¸Ð·ÐµÑÐ´Ð¸Ð½Ð¾Ð², ÐÐ°ÑÐ°Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ
UNET
VGG-16 (Gray)
machine learning
computer vision
colorization

description: ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð² Ð¿ÑÐ¾ÑÑÑÐ°Ð½ÑÑÐ²Ðµ LAB, Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐµÐ´ÑÑÐ² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ ÑÐµÑÑÑÑÐ¾ÑÐ¼ÐºÐ¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð° VGG-16 (Gray) Ð¸ ÐµÐ³Ð¾ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÑÑ Unet. Ð¦ÐµÐ»Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ - Ð¿ÑÐ¾Ð²ÐµÑÑÐ¸ ÑÑÐ°Ð²Ð½Ð¸ÑÐµÐ»ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ñ ÑÐ¾ÑÐºÐ¸ Ð·ÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¸ ÑÐµÑÑÑÑÐ¾ÑÐ¼ÐºÐ¾ÑÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸. Ð ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ ÑÐ°ÐºÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ ÐºÐ°Ðº: ÐºÐ¾Ð¼Ð¿Ð¸Ð»ÑÑÐ¸Ñ, Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÐµÑÐ½Ð¾-Ð±ÐµÐ»ÑÑ ÑÐ¸ÑÑÐ¾Ð²ÑÑ ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² VGG-16 (Gray), Ð¼ÐµÑÐ¾Ð´Ñ Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹, Ð¼ÐµÑÐ¾Ð´ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ ÑÐ¿ÑÑÐºÐ° Ð¿ÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¿Ð¾Ð»ÑÑÐµÐ½Ñ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ, Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ¸Ðµ, ÑÑÐ¾ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð¿ÑÐµÐ²Ð¾ÑÑÐ¾Ð´Ð¸Ñ Ð¾ÑÐ¸Ð³Ð¸Ð½Ð°Ð»ÑÐ½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð² ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ ÑÐºÐ¾Ð½Ð¾Ð¼Ð¸Ð¸ Ð¿Ð°Ð¼ÑÑÐ¸ Ð¸ Ð¿ÑÐ¸ ÑÑÐ¾Ð¼ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ñ Ð±Ð»Ð¸Ð·ÐºÑÑ Ð¿Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ñ ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ñ. ÐÐ° Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑÑÑ Ð¿ÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ¸ÑÑÐ¾Ð²ÑÑ ÑÐ½Ð¸Ð¼ÐºÐ¾Ð² Ð¸ Ð² Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸ÑÑ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÐºÐ¾Ð»Ð¾ÑÐ¸Ð·Ð°ÑÐ¸Ð¸.
description: The subject of the study is a comparative analysis of colorization methods in the LAB space, a study of the means of implementation and resource intensity of the VGG-16 (Gray) method and its modification by the Unet artificial neural network. The purpose of the study is to conduct a comparative analysis of colorization methods in terms of quality and resource intensity of processing. The work uses such methods as: compilation, automated method of colorization of black-and-white digital images VGG-16 (Gray), methods for designing artificial neural networks, the method of gradient descent in training. As a result of the work, experimental data were obtained showing that the modification of the chosen method is superior to the original method in terms of learning speed and memory savings, and at the same time, it performs colorization that is close in quality. The results can be used in the practice of processing digital images and in further studies of automatic colorization.