基于条件生成对抗网络的梯级表面高光去除方法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-11 Cooperative journals: 《计算机应用研究》

胡宇航 胡海洋 李忠金

Abstract： It is difficult for traditional highlight removal algorithms to effectively deal with the processing of stepped highlight images in the stepped palletizing of factory robots. To solve this problem, based on the knowledge of conditional generative adversarial network, this paper proposes a stepped surface highlight removal network model named MSDGC-GAN (Multi-scale Spatial dense gradient cascade generative adversarial network) . In this method, the Spatial Contextual Feature Dense Block (SCFDB) aims to deeply extract the spatial background information between pixel rows and columns. In addition, the multi-scale gradient cascade structure aims to compensate for the scale feature loss in network downsampling, and this structure can endow the model with multi-scale discriminative ability while stabilizing the training gradient distribution. Based on the analysis of the classical two-color reflectance model, we apply the maximum diffuse reflectance estimation to the loss function to supervise the network training. The experimental results show that the proposed method outperforms the compared methods in both the classical highlight dataset and the self-made stepped highlight image dataset.

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