Kavli Affiliate: Yi Zhou
| First 5 Authors: [#item_custom_name[1]], [#item_custom_name[2]], [#item_custom_name[3]], [#item_custom_name[4]], [#item_custom_name[5]]
| Summary:
RGB-NIR fusion is a promising method for low-light imaging. However,
high-intensity noise in low-light images amplifies the effect of structure
inconsistency between RGB-NIR images, which fails existing algorithms. To
handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net
(DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior
(DIP). The Deep Structure extracts clear structure details in deep multiscale
feature space rather than raw input space, which is more robust to noisy
inputs. Based on the deep structures from both RGB and NIR domains, we
introduce the DIP to leverage the structure inconsistency to guide the fusion
of RGB-NIR. Benefiting from this, the proposed DVN obtains high-quality
lowlight images without the visual artifacts. We also propose a new dataset
called Dark Vision Dataset (DVD), consisting of aligned RGB-NIR image pairs, as
the first public RGBNIR fusion benchmark. Quantitative and qualitative results
on the proposed benchmark show that DVN significantly outperforms other
comparison algorithms in PSNR and SSIM, especially in extremely low light
conditions.
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