Automatic segmentation and quantified analysis of meibomian glands from infrared images.

Purpose: An algorithm for automated segmentation of meibomian glands from infrared images obtained using a novel prototype infrared hand-held imager has been proposed in this study. Meibomian gland dysfunction (MGD) is quantified in terms of five clinically relevant metrics. A comparison of these metrics in patients with MGD has been presented against a sample of the normative healthy population.

Unified multiframe super-resolution of matte, foreground, and background.

Reconstruction of a super-resolved image from multiple frames and extraction of matte are two popular topics that have been solved independently. In this paper, we advocate a unified framework that assimilates matting within the super-resolution model. We show that joint estimation is advantageous, as super-resolved edge information helps in obtaining a sharp matte, while the matte in turn aids in resolving fine details.

Natural matting for degraded pictures.

A new approach for image matting is proposed based on the Kalman filter, to extract the matte and original foreground, despite the presence of noise in the observed image. Different filter formulations with a discontinuity-adaptive Markov random field prior are proposed for handling additive white Gaussian noise and film-grain noise.