Abstract: Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote ...
Abstract: Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient ...
Abstract: Change detection aims to detect changes of objects or scenes in remote sensing images, which is critical for observing the Earth’s surface. However, due to the insufficient correlation and ...
Posts from this topic will be added to your daily email digest and your homepage feed. Welcome to our end-of-year Decoder special! Senior producers Kate Cox and Nick Statt here. We’ve had a big year, ...
This project implements a Variational Autoencoder (VAE) for image generation. Unlike standard autoencoders, VAE learns a probabilistic latent space by encoding images to a distribution and sampling ...
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