Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAGAN: Adversarial Spatial-asymmetric Attention for Noisy Nona-Bayer
  Reconstruction   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.

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The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex