Honeycomb pattern removal for fiber bundle endomicroscopy based on a two-step iterative shrinkage thresholding algorithm
Jialin Liu, Wei Zhou, Baoteng Xu, Xibin Yang, Daxi Xiong
Abstract
Fiber bundle endomicroscopy has potential for facilitating high-resolution (HR) in vivo imaging. One of the main challenges of this technique is the improvement of image restoration for better visualization. In this paper, we propose to reconstruct a HR image without a fixed honeycomb pattern from a noisy observation image, which can be formulated as an inverse problem. We use the obtained fixed honeycomb pattern as a prior image and use a two-step iterative shrinkage thresholding algorithm with a total variation regularization to solve this problem. In addition to the fixed honeycomb pattern removal, our method can also improve spatial resolution. The feasibility of our method is demonstrated by the images obtained from the USAF target and spider silks. In each ease, our method recovers more details than that recovered by the conventional method. The proposed theoretical framework for the removal of the honeycomb pattern in this paper shows promising and wide applications for fiber bundle imaging.