Shandong Normal University

Tingxuan Hong

Changming Gu

Dengwang Li

Bin Jin

Xiangyu Zhai

Jie Xue

Pu Huang

Baolong Tian

Hua Lu

Xueyao Liu

A laparoscopic image smoke removal method based on a generative adversarial network, and belongs to the technical field of computer vision. The method includes: processing a laparoscopic image sample to be processed using a smoke mask segmentation network to acquire a smoke mask image; inputting the laparoscopic image sample to be processed and the smoke mask image into a smoke removal network, and extracting features of the laparoscopic image sample to be processed using a multi-level smoke feature extractor to acquire a light smoke feature vector and a heavy smoke feature vector; and acquiring, according to the light smoke feature vector, the heavy smoke feature vector and the smoke mask image, a smoke-free laparoscopic image by filtering out smoke information and maintaining a laparoscopic image by using a mask shielding effect. The method has the technical effects of robustness and ability of being embedded into a laparoscopic device for use.

Laparoscopic image smoke removal method based on generative adversarial network

Growing community of inventors

Shandong, China

Tingxuan Hong