模型可自动纠正乱序的三通道图像颜色顺序。
Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images
- 基于语义先验设计网络结构与损失函数，为每通道打分预测顺序。
- 在真实图像中准确识别并修正错乱的RGB/BGR顺序。
- 还能判断图像是否接近灰度，适合图像处理与视觉感知研究者。
本文提出一种名为Chanel-Orderer的模型，证明了对于随机排列通道顺序的三通道自然图像，仅通过特定的架构与损失函数设计，即可准确预测正确的通道顺序，并实现正确还原。该模型利用物体语义先验对三个通道分别评分，依据得分确定最优顺序。这一方法解决了常见问题：原本应为RGB格式的图像因误显示为BGR而需纠正。此外，该模型的副产物可判断图像是否为近似灰度（近单色）或彩色图像。研究表明，Chanel-Orderer的行为模拟了人类对自然世界色彩的视觉认知。
原文摘要 · Abstract (English)
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.
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