用改进的Pix2Pix GAN修复无人机拍摄图像中的视觉缺陷
Enhanced Pix2Pix GAN for Visual Defect Removal in UAV-Captured Images
- 基于改进Pix2Pix GAN，解决模式崩溃等常见问题
- 在自建航拍图像数据集上显著提升修复质量
- 适合需要高质量航拍图像的工业检测场景
本文提出一种神经网络，有效去除无人机拍摄图像中的视觉缺陷。该方法采用改进的Pix2Pix GAN架构，专门针对无人机影像中常见的模式崩溃等问题进行优化。通过在自建航拍照片数据集上的评估，验证了该方法在提升缺陷图像质量方面的有效性，能够生成更清晰、更精确的视觉结果，显著改善图像修复效果。
原文摘要 · Abstract (English)
This paper presents a neural network that effectively removes visual defects from UAV-captured images. It features an enhanced Pix2Pix GAN, specifically engineered to address visual defects in UAV imagery. The method incorporates advanced modifications to the Pix2Pix architecture, targeting prevalent issues such as mode collapse. The suggested method facilitates significant improvements in the quality of defected UAV images, yielding cleaner and more precise visual results. The effectiveness of the proposed approach is demonstrated through evaluation on a custom dataset of aerial photographs, highlighting its capability to refine and restore UAV imagery effectively.
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