SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction
Abstract
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: https://github.com/sharif-apu/SAGAN_BMVC21.
Keywords
Cite
@article{arxiv.2110.08619,
title = {SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer Reconstruction},
author = {S M A Sharif and Rizwan Ali Naqvi and Mithun Biswas},
journal= {arXiv preprint arXiv:2110.08619},
year = {2021}
}