<p>Seems in context of super resolution it's called pixel shuffle layer (but maybe it's just a special case of sub-pixel convolution).</p>
<p><a href="https://pytorch.org/docs/stable/generated/torch.nn.PixelShuffle.html" rel="nofollow noreferrer">https://pytorch.org/docs/stable/generated/torch.nn.PixelShuffle.html</a></p>
<p>Paper: <a href="https://arxiv.org/pdf/1609.05158.pdf" rel="nofollow noreferrer">Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network by Shi et. al (2016)</a></p>
What is sub-pixel convolution?
<p>I am working on a super-resolution project. And for now, I am using Transposed Convolution for upsampling the image. But I have heard a lot that sub-pixel convolutions do it better. So what is subpixel convolution and the math behind it? And Why it is better than transposed convolution? (some example would be great)</p>