A Stronger Stitching Algorithm for Fisheye Images based on Deblurring and Registration
This addresses the challenge of creating high-quality panoramic images from fisheye lenses for applications like surveillance or photography, but it is incremental as it builds on existing calibration and stitching techniques.
The paper tackles the problem of severe geometric distortion in fisheye images that interferes with image registration and stitching, by proposing a stronger stitching algorithm combining traditional methods with deep learning, resulting in panoramic images of superior quality as demonstrated experimentally.
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.