When building deep learning models for image analytics-related applications, we sometimes apply various types of operations to enhance the image, such as an image denoising operation.
In my study, we have images generated by physical simulations. In other words, the physical simulations generate matrices of dimensions such as 256*256, which can be visualized as an image as well.
I am trying to apply a deep learning model to perform some analysis over these physics-based images. In the pre-processing steps, I can always apply those image analytics related techniques to pre-process my images, but I am not sure whether it makes sense. For example, denoising or some other contrast enhancement operations can be used to improve the quality of images, like photos. But would it make sense to use them process the images generated by physics simulation?