DPGIR: SIFT Recovery from a Hazy Image
Shan Huang, Hao Chang, Wei Wu, Zhu Li · 2022 IEEE International Conference on Multimedia and Expo (ICME) · 2022
Scale-Invariant Feature Transform (SIFT) plays a significant role in vision applications. Indeed, even with the advent of deep learning, SIFT-based object re-identification has been found to be competitive in a variety of object and scene recognition challenges. This is a relatively robust method. But under severe imaging conditions like hazy, the haze greatly hinders SIFT detection, causing matching performance degradation. To solve the problem, in this paper we propose a deep learning SIFT recovery algorithm from a single hazy image with a two-task framework, namely, Difference of Gaussian (DoG) Pyramid and Gradient Image Recovery (DPGIR). One task is to recover the dehazed DoG pyramid of a hazy image, and the other is to recover its dehazed Gaussian gradients. This scheme to set different objectives for SIFT detection and description leads to very robust performance. Compared with state-of-the-art methods, experimental results demonstrate that our proposed algorithm recovers more SIFT key points.