We propose a framework for scene-based nonuniformity correction (NUC) and nonuniformity correction and enhancement (NUCE) that is required for focal-plane array-like sensors to obtain clean and enhanced-quality images. The core of the proposed framework is a novel registration-based nonuniformity correction super-resolution (NUCSR) method that is bootstrapped by statistical scene-based NUC methods. Based on a comprehensive imaging model and an accurate parametric motion estimation, we are able to remove severe/structured nonuniformity and in the presence of subpixel motion to simultaneously improve image resolution. One important feature of our NUCSR method is the adoption of a parametric motion model that allows us to (1) handle many practical scenarios where parametric motions are present and (2) carry out perfect super-resolution in principle by exploring available subpixel motions. Experiments with real data demonstrate the efficiency of the proposed NUCE framework and the effectiveness of the NUCSR method.
© 2008 Optical Society of America
Original Manuscript: September 24, 2007
Revised Manuscript: February 12, 2008
Manuscript Accepted: March 17, 2008
Published: June 24, 2008
Wenyi Zhao and Chao Zhang, "Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion," J. Opt. Soc. Am. A 25, 1668-1681 (2008)