In this article ootnote{https://debuggercafe.com/reducing-image-distortion-using-spatial-transformer-network/}, we will learn how we can reduce distortion in images using the Spatial Transformer Network (STN) using the PyTorch deep learning library.
Figure 1 shows the results of applying STN to the distorted MNIST dataset. After applying STN to the distorted images, we can see that the images spatially more plausible and readable.
If you are new to the topic of Spatial Transformer Networks, then I highly recommend that you read my previous article. You will get an introduction to Spatial Transformer Networks with all the details about the network’s architecture as well. You will also get hands-on experience by applying STNs on the CIFAR10 images and visualizing the results yourself.
Dryad, G. (2021). Image Calibration with Spatial Transformer Network. Afribary. Retrieved from https://tracking.afribary.com/works/image-calibration-with-spatial-transformer-network
Dryad, George "Image Calibration with Spatial Transformer Network" Afribary. Afribary, 27 Nov. 2021, https://tracking.afribary.com/works/image-calibration-with-spatial-transformer-network. Accessed 24 Nov. 2024.
Dryad, George . "Image Calibration with Spatial Transformer Network". Afribary, Afribary, 27 Nov. 2021. Web. 24 Nov. 2024. < https://tracking.afribary.com/works/image-calibration-with-spatial-transformer-network >.
Dryad, George . "Image Calibration with Spatial Transformer Network" Afribary (2021). Accessed November 24, 2024. https://tracking.afribary.com/works/image-calibration-with-spatial-transformer-network