the feasibility of image processing techniques for physics based images 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?
I think your intuition is correct, that it doesn't make sense to "clean" the matrices, if they are generated perfectly.  I agree with @SyedAliHamza that noise should be eliminated as much as possible in the simulation.

On the other hand, depending on what analysis you are trying to do, you may want your model to generalize better. In that case, it would be good to apply some data augmentation, meaning adding some noise or other types of augmentation to your matrices.