Depixelation of coherent fiber bundle endoscopy based on learning patterns of image prior.

We present an iterative method of eliminating pixelation artifacts from an endoscopic image acquired from a coherent fiber bundle imager. Our proposed approach for decoupling the honeycomb effect from the obtained sample image was formulated by using the prior probability for an approximate Bayesian framework in which the ideal complete image can be estimated by maximizing the posterior probability from the observed image. The maximization of the posterior probability from the original mask image (the mirrored fiber bundle imager structure) and the observed image (the sample image of the United States Air Force chart) has been performed by learning the image priors in the space of Markov random fields. By iteratively estimating the probability distribution, we reduced the noise effects from the mask image and recovered the ideal shape of the image. This method was efficient for automatically learning the sliding patch from the combination of projected kernels. The mask and observed images were obtained from en face images of the Fourier domain optical coherence tomography based on a common path interferometry scheme.