A Multi-Scale Fusion and Transformer Based Registration Guided Speckle Noise Reduction for OCT Images
Zhiwei Tan, Fei Shi, Yi Zhou, Jingcheng Wang, Meng Wang, Yuanyuan Peng, Kai Xu, Ming Liu, Xinjian Chen
Abstract
Optical coherence tomography (OCT) images are inevitably affected by speckle noise because OCT is based on low-coherence interference. Multi-frame averaging is one of the effective methods to reduce speckle noise. Before averaging, the misalignment between images must be calibrated. In this paper, in order to reduce misalignment between images caused during the acquisition, a novel multi-scale fusion and Transformer based (MsFTMorph) method is proposed for deformable retinal OCT image registration. The proposed method captures global connectivity and locality with convolutional vision transformer and also incorporates a multi-resolution fusion strategy for learning the global affine transformation. Comparative experiments with other state-of-the-art registration methods demonstrate that the proposed method achieves higher registration accuracy. Guided by the registration, subsequent multi-frame averaging shows better results in speckle noise reduction. The noise is suppressed while the edges can be preserved. In addition, our proposed method has strong cross-domain generalization, which can be directly applied to images acquired by different scanners with different modes.