Tile art image generation using parallel greedy algorithm on the GPU and its approximation with machine learning
Tile art image generation using parallel greedy algorithm on the GPU and its approximation with machine learning
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DOI：
10.1002/cpe.5623
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Naoki Matsumura;Hiroki Tokura;Yuki Kuroda;Yasuaki Ito;K. Nakano
中科院分区：
文献类型：
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作者：
Naoki Matsumura;Hiroki Tokura;Yuki Kuroda;Yasuaki Ito;K. Nakano
瓷砖图像生成是非遗传学的渲染方法之一。基于人类视觉系统的特征，以优化生成的图像，以缩短计算时间，我们显示了平行算法及其GPU加速技术在NVIDIA TESLA V100 GPU上实施。纸张是在学习通过贪婪方法产生的瓷砖艺术图像后使用机器学习的近似方法。作为训练数据集，它可以生成用瓷砖模式来填充原始图像的瓷砖艺术图像。使用此技术，我们可以生成具有透明瓷砖形状的瓷砖艺术图像。
Tile art image generation is one of the non‐photorealistic rendering methods. The generated digital image resembles artistic representation given digital photos and illustrations. The first contribution of this paper is to propose a tile image generation based on the greedy approach. The greedy approach is based on the characteristic of the human visual system to optimize generated images. In addition, to shorten the computation time, we show the parallel algorithm and its GPU acceleration technique. We have implemented it on NVIDIA Tesla V100 GPU. The experimental result shows that the GPU implementation attains a speed‐up factor of 318 and 16.19 over the sequential CPU implementation and the parallel multi‐core CPU implementation with 160 threads, respectively. The second contribution of this paper is to propose an approximation method using machine learning with deep neural networks. After learning the network with the tile art images generated by the greedy approach as training dataset, it can generate tile art images that well‐reproduce the original images with tile patterns. Moreover, we show an additional machine learning technique by repeating the forwarding computation for the generated tile art image as an input image. As a result, using this technique, we can generate a tile art images with clear shape of tiles.