Universal Infrared Image Nonuniformity Correction via Stripe-Aware Attention Network

Abstract

Infrared image nonuniformity correction aims to remove the column-wise stripe noise. Most existing methods just consider stripe noise whereas failing to handle real captured nonuniformity, as directional characteristic of stripe is severely disrupted by random Gaussian noise. Moreover, deep learning-based methods proposed in recent years are blocked by limited receptive field thus cannot accurately distinguish vertical structure and vertical stripes. To address these issues, we propose a universal infrared image nonuniformity correction method based on stripe-aware attention network. We seek to improve the performance of our algorithm by first restoring the damaged stripe directional characteristics, then maximizing the utilization of the prior characteristics. On the one hand, we construct the two-stage framework, in which denoising network is firstly applied to eliminate Gaussian noise and preserve stripes as scene information. As a result, the prior directional characteristics are restored, thereby enhancing the ability of subsequent sub-network to perceive stripe noise. On the other hand, due to the distinct long-range pixel correlations of vertical structures and vertical textures, we introduce a column-wise stripe attention mechanism (CSA) that can capture long-range dependencies of target pixels in the vertical direction. This significantly improves the discriminative ability of algorithm towards vertical structures and stripes, with minimal computational cost. Extensive experiments show that the proposed method can achieve promising results and has better universality for different infrared scenarios.