train on a hyperspectral image stack and predict what a new image would look like if I wanted to use a different channel

Let's say I have a stack of 4 grayscale images (they're all of the same thing, just different channels) in a 3D array. Each pixel (element index of each 2D slice of the 3D array) will thus have a varied set of intensities, one in each channel.

If these images are really basic, is there an easy way to create a model such that if I take a new image in one of these channels, I can predict what it WOULD look like in a different channel? Or if I had a new stack but was missing a slice, could I recreate it? I know there are a few cycleGANs out there that can do this with histological images, but for grayscale images do I really need to do this?

Answer 1

[Avadhoot]

Hi @AbioEngineer,

Your question boils down to whether you can train a model which when given a single channel image, can generate the same image in a different channel. This sounds like an image-to-image translation problem. This can be solved using various techniques in deep learning and machine learning. The most popular ones are convolutional neural networks(CNNs), autoencoder-based models and transformers. Since you already mentioned CycleGANs, I believe you have a rather complex and thorough implementation in mind. In such case a transformer-based model would be the perfect solution for this problem.

This problem can also be interpreted as a missing slice reconstruction problem wherein you feed the available channels into the transformer and it outputs the missing channel image. The input can be provided either by treating each slice as a separate channel or inputting the spatial information alongside the slice data so that it pertains closely to a hyperspectral image.

In both the cases the methodology of using the transformer will remain same. I have mentioned the model construction and training phases below.

1. Model Construction:

Transformers can be applied to image-related tasks using a structure similar to Vision Transformers (ViT). In this approach, an image (or a set of images) is divided into patches, and these patches are then flattened and processed through a series of transformer blocks that model the relationships between different parts of the image

For modelling phase you could refer to Vision Transformers(ViT) as it pertains closely to your problem.

2. Model Training:

To use a transformer for your task, you would need a dataset consisting of pairs of input images (or image stacks) and the corresponding target channel or slice. During training, you would optimize the model to minimize the difference between its predictions and the actual target images. This could involve using a loss function suitable for image data, such as mean squared error (MSE) for regression tasks.

Transformers would enable you to model the complex dependencies within the images and also use the spatial context of the hyperspectral images to your advantage.

I hope this helps.