Specular Reflections Removal for Endoscopic Image Sequences With Adaptive-RPCA Decomposition.

Specular reflections (i.e., highlight) always exist in endoscopic images, and they can severely disturb surgeons' observation and judgment. In an augmented reality (AR)-based surgery navigation system, the highlight may also lead to the failure of feature extraction or registration. In this paper, we propose an adaptive robust principal component analysis (Adaptive-RPCA) method to remove the specular reflections in endoscopic image sequences. It can iteratively optimize the sparse part parameter during RPCA decomposition. In this new approach, we first adaptively detect the highlight image based on pixels. With the proposed distance metric algorithm, it then automatically measures the similarity distance between the sparse result image and the detected highlight image. Finally, the low-rank and sparse results are obtained by enforcing the similarity distance between the two types of images to fall within a certain range. Our method has been verified by multiple different types of endoscopic image sequences in minimally invasive surgery (MIS). The experiments and clinical blind tests demonstrate that the new Adaptive-RPCA method can obtain the optimal sparse decomposition parameters directly and can generate robust highlight removal results. Compared with the state-of-the-art approaches, the proposed method not only achieves the better highlight removal results but also can adaptively process image sequences.