Nona-Bayer colour filter array (CFA) pattern is considered one of the most
viable alternatives to traditional Bayer patterns. Despite the substantial
advantages, such non-Bayer CFA patterns are susceptible to produce visual
artefacts while reconstructing RGB images from noisy sensor data. This study
addresses the challenges of learning RGB image reconstruction from noisy
Nona-Bayer CFA comprehensively. We propose a novel spatial-asymmetric attention
module to jointly learn bi-direction transformation and large-kernel global
attention to reduce the visual artefacts. We combine our proposed module with
adversarial learning to produce plausible images from Nona-Bayer CFA. The
feasibility of the proposed method has been verified and compared with the
state-of-the-art image reconstruction method. The experiments reveal that the
proposed method can reconstruct RGB images from noisy Nona-Bayer CFA without
producing any visually disturbing artefacts. Also, it can outperform the
state-of-the-art image reconstruction method in both qualitative and
quantitative comparison. Code available:
this https URL

本文介绍了如何使用新型空间 - 不对称注意力模块和对抗学习技术综合学习 Nona-Bayer CFA 噪声图像中的 RGB 图像重建，结果表明该方法在定性和定量比较上均优于现有的图像重建方法，并且可以避免产生任何视觉上妨碍的图像伪影。