Distance Constrained Sparse Representation Approach for Scene Based Nonuniformity Correction in Infrared Imaging

Abstract

A Scene-Based Nonuniformity Correction (SBNUC) approach is proposed for the correction of fixed-pattern-noise (FPN) in infrared image arrays. This method diverges from conventional neural-network based techniques by eschewing spatial filters for the computation of the desired image. Instead, it harnesses the sparse representation of the estimated irradiance within a hypersphere of adaptive radius, adhering to a distance constraint. The proposed method conceptualizes images as points in a high-dimensional space, with the distance between two images defined by their root mean square difference. The adaptive radius is derived from a sophisticated selection criterion, inversely related to the distance between the noisy input image and the estimated irradiance. This approach ensures edge preservation without excessive blurring while discarding FPN as redundant in the representation coefficients. We propose an adaptive learning rate tailored to the desired image from sparse representation, which contrasts with traditional learning rates that rely on local spatial standard deviation. This new learning rate is determined by the pixel-wise distance between the input image and the desired image, integrating a new motion detection criterion to synchronize the update of correction coefficients across pixels. Our experimental results demonstrate that the method proposed in this paper achieves better removal of fixed-pattern-noise in both simulated and real infrared image sequences compared to the methods used for comparison.

Keywords

• Adaptive learning rate
• infrared imaging
• nonuniformity correction
• scene based
• sparse representation