一种基于去模糊与配准的更强鱼眼图像拼接算法
A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration
AI总结：
针对鱼眼图像几何畸变影响拼接的问题，提出结合深度学习的拼接算法，用ANAFNet去模糊、OFG算法提升配准精度，实现高质量全景拼接。
摘要：
鱼眼镜头适用于全景成像，具备视场大、成本低的突出优势。但鱼眼图像存在严重的几何畸变，会对图像配准与拼接阶段造成干扰。针对这一缺陷，我们结合传统图像处理方法与深度学习，设计了一种性能更强的鱼眼图像拼接算法。在鱼眼图像校正阶段，我们提出基于注意力的无非线性激活网络（Attention-based Nonlinear Activation Free Network，ANAFNet），对经张正友标定法校正的鱼眼图像进行去模糊处理。具体而言，ANAFNet采用基于卷积神经网络的经典单阶段U型架构，结合软注意力技术，可有效从模糊图像中恢复出清晰图像。在图像配准部分，我们提出综合图像匹配算法ORB-FREAK-GMS（OFG），以提升图像配准的精度。实验结果表明，通过我们的方法能够获得由鱼眼图像拼接而成的质量更优的全景图像。
英文摘要：
Fisheye lens, which is suitable for panoramic imaging, has the prominent advantage of a large field of view and low cost. However, the fisheye image has a severe geometric distortion which may interfere with the stage of image registration and stitching. Aiming to resolve this drawback, we devise a stronger stitching algorithm for fisheye images by combining the traditional image processing method with deep learning. In the stage of fisheye image correction, we propose the Attention-based Nonlinear Activation Free Network (ANAFNet) to deblur fisheye images corrected by Zhang calibration method. Specifically, ANAFNet adopts the classical single-stage U-shaped architecture based on convolutional neural networks with soft-attention technique and it can restore a sharp image from a blurred image effectively. In the part of image registration, we propose the ORB-FREAK-GMS (OFG), a comprehensive image matching algorithm, to improve the accuracy of image registration. Experimental results demonstrate that panoramic images of superior quality stitching by fisheye images can be obtained through our method.
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。
