# Sand Painting Generation Based on Convolutional Neural Networks

## TL;DR

This paper introduces a new method using neural networks to create sand paintings with clearer images and less background noise.

## Contribution

A novel sand painting generation approach using CNNs that improves object clarity and reduces background noise.

## Key findings

- The proposed method segments main objects and uses edge detection for better sand painting results.
- The approach outperforms previous methods in visual quality and clarity of generated sand paintings.

## Abstract

Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation based on convolutional neural networks. The proposed approach aims to improve sand painting generation via neural style transfer, which can address the problem of blurred objects. Furthermore, it can reduce background noise caused by neural style transfers. First, we segment the main objects from the content image. Subsequently, we perform close–open filtering operations on the content image to obtain smooth images. Subsequently, we perform Sobel edge detection to process the images and obtain edge maps. Based on these edge maps and the input style image, we perform neural style transfer to generate sand painting images. Finally, we integrate the generated images to obtain the final stylized sand painting image. The results show that the proposed approach yields good visual effects from sand paintings. Moreover, the proposed approach achieves better visual effects for sand painting than the previous method.