使用由嵌入式暗通道引导的图像到图像平移来消除腹腔镜手术图像的烟雾
Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel
摘要：
在腹腔镜手术中，$CO_2$注射和切割工具产生的烟雾会严重降低图像的可见性，从而降低器官和组织的可见性。这种可见性的缺乏增加了手术时间，甚至增加了外科医生犯错误的可能性，然后对患者的健康产生负面影响。本文介绍了一种新的计算方法，以消除烟雾的影响。所提出的方法是基于图像到图像的条件生成对抗网络，其中暗通道被用作嵌入式引导掩码。获得的实验结果进行评估和定量比较与其他去烟和去雾国家的最先进的方法，使用的峰值信噪比（PSNR）和结构相似性（SSIM）指数的指标。基于这些指标，它被发现，所提出的方法具有改善的性能相比，国家的最先进的。此外，我们的方法所需的处理时间是每秒92帧，因此，它可以应用于实时医疗系统通过嵌入式设备。
英文摘要：
In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
