Endoscopic video deblurring via synthesis

Abstract

Endoscopic videos have been widely used for stomach diagnoses. However, endoscopic devices often capture videos with motion blurs, due to the dimly-lit environment and the camera shakiness during the capturing, which severely disturbs the diagnoses. In this paper, we present a framework that can restore blurry frames by synthesizing image details from the nearby sharp frames. Specifically, the blurry frame and their corresponding nearby sharp frames are identified according to the image gradient sharpness. To restore one blurry frame, a non-parametric mesh-based motion model is proposed to align the sharp frame to the blurry frame. The motion model leverages motions from image feature matches and optical flows, which yields high quality alignments to overcome challenges such as noisy, blurry, reflective and textureless interferences. After the alignment, the deblurred frame is synthesized by matching patches locally between the blurry frame and the aligned sharp frame. Without the estimation of blur kernels, we show that it is possible to directly compare a blurry patch against the sharp patches for the nearest neighbor matches in endoscopic images. The experiments demonstrate the effectiveness of our algorithm.