A Stronger Stitching Algorithm for Fisheye Images based on Deblurring
  and Registration
Jing Hao Jingming Xie Jinyuan Zhang Moyun Liu
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.