Scholarlink

<p>To reduce the considerable risk of infection associated with traditional open surgery, robot-assisted laparoscopic surgery is employed. However, during the robot-assisted surgery, smoke is produced, which lowers image visibility; removing smoke from images is comes under image dehazing. Several image dehazing methods based on different architectures have been created to enhance damaged images. We presented an attention-based Y-Net (AY-Net) to help in the surgery by dehazing smoked laparoscopic surgical images. The AY-Net architecture consists of an encoder, bottleneck, decoder, and mixed decoder, with skip connections between the encoder, decoder, and mixed decoder. The encoder contains encoder layers, and the decoder contains decoder layers, both of which have CNN block sublayers and the mixed decoder has a mixed layer has its sublayers. The proposed Attention Y-Net performed well, on Outdoor, Dense-Haze, and DeSmoke-LAP dataset when compared to other current approaches for image dehazing, both quantitatively and visually.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AY-Net: Attention Y-Net–Based Dehazing in Laproscopic Surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

查看全文
  • Banala Revanth,
  • Sanjay K. Dwivedi,
  • Manoj Kumar

摘要

To reduce the considerable risk of infection associated with traditional open surgery, robot-assisted laparoscopic surgery is employed. However, during the robot-assisted surgery, smoke is produced, which lowers image visibility; removing smoke from images is comes under image dehazing. Several image dehazing methods based on different architectures have been created to enhance damaged images. We presented an attention-based Y-Net (AY-Net) to help in the surgery by dehazing smoked laparoscopic surgical images. The AY-Net architecture consists of an encoder, bottleneck, decoder, and mixed decoder, with skip connections between the encoder, decoder, and mixed decoder. The encoder contains encoder layers, and the decoder contains decoder layers, both of which have CNN block sublayers and the mixed decoder has a mixed layer has its sublayers. The proposed Attention Y-Net performed well, on Outdoor, Dense-Haze, and DeSmoke-LAP dataset when compared to other current approaches for image dehazing, both quantitatively and visually.