Image Calibration with Spatial Transformer Network
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In this articleootnote{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.
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APA
Dryad, G. (2026). Image Calibration with Spatial Transformer Network. Afribary. Retrieved June 14, 2026, from http://library.afribary.com/works/image-calibration-with-spatial-transformer-network
MLA
Dryad, George. "Image Calibration with Spatial Transformer Network." Afribary, 7 Jun. 2026, http://library.afribary.com/works/image-calibration-with-spatial-transformer-network. Accessed June 14, 2026.
Chicago
Dryad, George. "Image Calibration with Spatial Transformer Network." Afribary (2026). Accessed June 14, 2026. http://library.afribary.com/works/image-calibration-with-spatial-transformer-network