title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ 2,5D Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¼Ð¸ÐºÑÐ¾ÑÑÑÑÐºÑÑÑÑ Ð¿Ð¾Ð²ÐµÑÑ Ð½Ð¾ÑÑÐ¸ Ð²ÑÑÐ¾ÐºÐ¾Ð³Ð¾ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Super-resolution for 2.5D height images of microstructured surfaces using neural networks
Creators: name: ÐÐ¾ÐµÐ»ÐºÐµÑ, Ð¥ÐµÐ½Ð´ÑÐ¸Ðº
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: 2020
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÑÐ¿ÐµÑ-ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ðµ
super resolution

description: Ð ÑÐ²ÑÐ·Ð¸ Ñ ÑÐ°ÑÑÑÑÐ¸Ð¼Ð¸ ÑÑÐµÐ±Ð¾Ð²Ð°Ð½Ð¸ÑÐ¼Ð¸ Ðº ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ°Ð½Ð¸ÑÐµÑÐºÐ¸Ñ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð² ÑÐ¾ÑÐ½Ð¾Ðµ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸Ðº Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸ ÑÑÐ°Ð½Ð¾Ð²Ð¸ÑÑÑ Ð²ÑÐµ Ð±Ð¾Ð»ÐµÐµ Ð²Ð°Ð¶Ð½ÑÐ¼. Ð¢Ð°ÐºÐ¸Ðµ ÑÐ°ÑÑÐµÑÐ½ÑÐµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ Ð¿Ð¾ÑÑÐ¾ÑÐ½Ð½Ð¾ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð² Ð¿ÑÐ¾ÑÐµÑÑÐµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²Ð°. ÐÐ´Ð½Ð°ÐºÐ¾, Ð´Ð»Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð¾Ð¹ÑÑÐ² Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸, Ñ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð¼Ð¾Ð¶Ð½Ð¾ Ð¸Ð·Ð¼ÐµÑÐ¸ÑÑ ÑÐ¾Ð»ÑÐºÐ¾ Ð½ÐµÐ±Ð¾Ð»ÑÑÐ¾Ð¹ ÑÑÐ°ÑÑÐ¾Ðº Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸. Ð­ÑÐ¾ ÑÐ²ÑÐ·Ð°Ð½Ð¾ Ñ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¸Ð²Ð°. Ð¡Ð»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾, Ð´Ð»Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸ Ð½ÐµÐ¾Ð±ÑÐ¾Ð´Ð¸Ð¼Ñ Ð¼Ð½Ð¾Ð³Ð¾ÐºÑÐ°ÑÐ½ÑÐµ Ð¸Ð·Ð¼ÐµÑÐµÐ½Ð¸Ñ. ÐÐ´ÐµÑ Ð¿ÑÐµÐ¾Ð´Ð¾Ð»ÐµÐ½Ð¸Ñ ÑÑÐ¾Ð¹ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð² ÑÐ¾Ð¼, ÑÑÐ¾Ð±Ñ Ð¸Ð·Ð¼ÐµÑÐ¸ÑÑ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÑ Ñ Ð½Ð¸Ð·ÐºÐ¸Ð¼ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸ÐµÐ¼ Ð¸ Ð·Ð°ÑÐµÐ¼ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾ ÑÐ²ÐµÐ»Ð¸ÑÐ¸ÑÑ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ðµ. ÐÑÐ¾ÑÐµÑÑ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ñ Ð½Ð°Ð·ÑÐ²Ð°ÐµÑÑÑ ÑÑÐ¿ÐµÑ-ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸ÐµÐ¼. Ð¢ÐµÐ¼Ð° Ð¸ Ð½Ð°Ð·Ð²Ð°Ð½Ð¸Ðµ ÑÑÐ¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ 2,5D Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¼Ð¸ÐºÑÐ¾ÑÑÑÑÐºÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐµÐ¹ Ð²ÑÑÐ¾ÐºÐ¾Ð³Ð¾ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ñ. Ð­ÑÐ° Ð´Ð¸Ð¿Ð»Ð¾Ð¼Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÑÐ°Ð·ÑÐµÑÐµÐ½Ð¸Ñ ÐºÐ°ÑÑ Ð²ÑÑÐ¾Ñ 2.5D Ñ ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐµÐ¹. Ð¡ ÑÑÐ¾Ð¹ ÑÐµÐ»ÑÑ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð¸ ÑÑÐ½ÐºÑÐ¸Ð¹ Ð¿Ð¾ÑÐµÑÑ Ð½Ð° Ð´Ð²ÑÑ ÑÐ°Ð·Ð½ÑÑ Ð½Ð°Ð±Ð¾ÑÐ°Ñ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð° ÑÐµÐ¿Ð¾ÑÐºÐ° Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² Ð´Ð»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÐºÐ°ÑÑ Ð²ÑÑÐ¾Ñ 2.5D Ð¿ÐµÑÐµÐ´ Ð¸Ñ ÑÐ¾ÑÑÐ°Ð½ÐµÐ½Ð¸ÐµÐ¼ Ð² Ð½Ð°Ð±Ð¾ÑÐµ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ»Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ ÑÐµÑÐµÐ½Ð¸Ðµ ÑÐ¾ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐ¼Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐ¼Ð¸ ÑÐµÑÑÐ¼Ð¸ (CNN), Ð² ÑÐ¾ Ð²ÑÐµÐ¼Ñ ÐºÐ°Ðº Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐ½ÐºÑÐ¸Ð¹ Ð¿Ð¾ÑÐµÑÑ, ÑÐ¾Ð±ÑÑÐ²ÐµÐ½Ð½Ð°Ñ ÑÑÐ½ÐºÑÐ¸Ñ Ð¿Ð¾ÑÐµÑÑ, ÑÐ¾ÑÐµÑÐ°ÑÑÐ°Ñ Ð¿Ð¾ÑÐµÑÑ I1 ÑÐ¾ ÑÐ²Ð¾Ð¹ÑÑÐ²Ð°Ð¼Ð¸ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸, Ð¾Ð±ÐµÑÐ°ÐµÑ Ð»ÑÑÑÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ. Ð¡ÑÐ°Ð²Ð½Ð¸Ð²Ð°Ñ ÑÐ°Ð·Ð½ÑÐµ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÑ CNN, ÑÑÐ°Ð½Ð¾Ð²Ð¸ÑÑÑ Ð¾ÑÐµÐ²Ð¸Ð´Ð½ÑÐ¼, ÑÑÐ¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ°Ð½Ð¸Ð·Ð¼Ð° Ð²Ð½Ð¸Ð¼Ð°Ð½Ð¸Ñ Ðº ÐºÐ°Ð½Ð°Ð»Ñ ÑÐ²ÐµÐ»Ð¸ÑÐ¸Ð²Ð°ÐµÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ. ÐÐ¾Ð¼Ð¸Ð¼Ð¾ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑ Ð¸ ÑÑÐ½ÐºÑÐ¸Ð¹ Ð¿Ð¾ÑÐµÑÑ, ÑÑÐ° ÑÐ°Ð±Ð¾ÑÐ° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐµÐ´Ð¾ÑÑÐ°Ð²Ð»ÑÐµÑ Ð¿Ð¾Ð´ÑÐ¾Ð±Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ²Ð¾Ð¹ÑÑÐ² Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐ¸, Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ¸Ñ, ÑÑÐ¾ ÑÐ¾ÑÐ½Ð¾ÑÑÑ Ð³Ð»Ð°Ð´ÐºÐ¸Ñ Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÐµÐ¹ Ð²ÑÑÐµ, ÑÐµÐ¼ ÑÐµÑÐ¾ÑÐ¾Ð²Ð°ÑÑÑ.
description: Due to the growing demands on the functionalities of mechanical components, the actual production of the surface characteristics becomes more crucial. The manufacturing process needs to control such designed characteristics at all time during the manufacturing process. However, to examine the surface properties in the needed accuracy, only a small segment of the surface can be measured. This disadvantage is due to the needed magnification of the objective. Therefore, multiple measurements are necessary for an examination of the surface. The idea to overcome this problem is to measure the surface in a low resolution and increase the resolution in a second step artificially. The process of increasing the resolution is called Super Resolution. The topic and title of this work are Super-Resolution for 2.5D Height Images of Microstructured Surfaces using Neural Network.This thesis deals with the implementation of neural networks to increase the resolution of 2.5D height images from technical surfaces. For this purpose, this thesis provides a comparison of various network architectures and loss functions on two different data sets. Also introduces this work a process chain to process the 2.5D height images before saving them in the data set. For the architecture, a solution with convolutional neural networks (CNN) offers itself, while by the loss functions the pixel loss functions, like the l1 loss and an own loss function, which combines the l1 loss with surface properties, promises the best results. By comparing different CNN architectures, it stands out that using a Channel Attention mechanism increases the performance. In addition to the architectures and lossfunctions, this work also provides a detailed analysis of the performance behaviourof different surface properties showing that the accuracy depends on the propertiesand is superior for surfaces without sharp transitions like to find on smooth surfaces.