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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.13021 |
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| _version_ | 1866910705654956032 |
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| author | Li, Shen Jiang, Lei Wang, Wei Hu, Hongwei Li, Liang |
| author_facet | Li, Shen Jiang, Lei Wang, Wei Hu, Hongwei Li, Liang |
| contents | This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, Chanel-Orderer learns to score each of the three channels with the priors of object semantics and uses the resulting scores to predict the channel ordering. This brings up benefits into a typical scenario where an \texttt{RGB} image is often mis-displayed in the \texttt{BGR} format and needs to be corrected into the right order. Furthermore, as a byproduct, the resulting model Chanel-Orderer is able to tell whether a given image is a near-gray-scale image (near-monochromatic) or not (polychromatic). Our research suggests that Chanel-Orderer mimics human visual coloring of our physical natural world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images Li, Shen Jiang, Lei Wang, Wei Hu, Hongwei Li, Liang Computer Vision and Pattern Recognition This paper shows a proof-of-concept that, given a typical 3-channel images but in a randomly permuted channel order, a model (termed as Chanel-Orderer) with ad-hoc inductive biases in terms of both architecture and loss functions can accurately predict the channel ordering and knows how to make it right. Specifically, Chanel-Orderer learns to score each of the three channels with the priors of object semantics and uses the resulting scores to predict the channel ordering. This brings up benefits into a typical scenario where an \texttt{RGB} image is often mis-displayed in the \texttt{BGR} format and needs to be corrected into the right order. Furthermore, as a byproduct, the resulting model Chanel-Orderer is able to tell whether a given image is a near-gray-scale image (near-monochromatic) or not (polychromatic). Our research suggests that Chanel-Orderer mimics human visual coloring of our physical natural world. |
| title | Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.13021 |