Chanel-Orderer: A Channel-Ordering Predictor for Tri-Channel Natural Images
This addresses a specific issue in image processing for scenarios where channel misordering occurs, but it is incremental as it builds on existing methods for channel correction.
The paper tackles the problem of correcting randomly permuted channel orders in tri-channel natural images, such as RGB images displayed in BGR format, by introducing Chanel-Orderer, a model that predicts the correct ordering with accuracy, and as a byproduct, identifies near-gray-scale images.
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.