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Fisheye lens, which is suitable for panoramic imaging, has the prominentadvantage of a large field of view and low cost. However, the fisheye image hasa severe geometric distortion which may interfere with the stage of imageregistration and stitching. Aiming to resolve this drawback, we devise astronger stitching algorithm for fisheye images by combining the traditionalimage processing method with deep learning. In the stage of fisheye imagecorrection, 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 architecturebased on convolutional neural networks with soft-attention technique and it canrestore a sharp image from a blurred image effectively. In the part of imageregistration, we propose the ORB-FREAK-GMS (OFG), a comprehensive imagematching algorithm, to improve the accuracy of image registration. Experimentalresults demonstrate that panoramic images of superior quality stitching byfisheye images can be obtained through our method.

A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration