Learning deep illumination-robust features from multispectral filter array images
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912131428909056 |
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| author | Amziane, Anis |
| author_facet | Amziane, Anis |
| contents | Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA), capture multiple spectral bands in a single shot, resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estimated from the raw one through \textit{demosaicing}, which inevitably introduces spatio-spectral artifacts. Moreover, training on fully-defined MS images can be computationally intensive, particularly with deep neural networks (DNNs), and may result in features lacking discrimination power due to suboptimal learning of spatio-spectral interactions. Furthermore, outdoor MS image acquisition occurs under varying lighting conditions, leading to illumination-dependent features. This paper presents an original approach to learn discriminant and illumination-robust features directly from raw images. It involves: \textit{raw spectral constancy} to mitigate the impact of illumination, \textit{MSFA-preserving} transformations suited for raw image augmentation to train DNNs on diverse raw textures, and \textit{raw-mixing} to capture discriminant spatio-spectral interactions in raw images. Experiments on MS image classification show that our approach outperforms both handcrafted and recent deep learning-based methods, while also requiring significantly less computational effort. The source code is available at https://github.com/AnisAmziane/RawTexture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15472 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning deep illumination-robust features from multispectral filter array images Amziane, Anis Computer Vision and Pattern Recognition Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA), capture multiple spectral bands in a single shot, resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estimated from the raw one through \textit{demosaicing}, which inevitably introduces spatio-spectral artifacts. Moreover, training on fully-defined MS images can be computationally intensive, particularly with deep neural networks (DNNs), and may result in features lacking discrimination power due to suboptimal learning of spatio-spectral interactions. Furthermore, outdoor MS image acquisition occurs under varying lighting conditions, leading to illumination-dependent features. This paper presents an original approach to learn discriminant and illumination-robust features directly from raw images. It involves: \textit{raw spectral constancy} to mitigate the impact of illumination, \textit{MSFA-preserving} transformations suited for raw image augmentation to train DNNs on diverse raw textures, and \textit{raw-mixing} to capture discriminant spatio-spectral interactions in raw images. Experiments on MS image classification show that our approach outperforms both handcrafted and recent deep learning-based methods, while also requiring significantly less computational effort. The source code is available at https://github.com/AnisAmziane/RawTexture. |
| title | Learning deep illumination-robust features from multispectral filter array images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.15472 |