A variational autoencoder inspired unsupervised remote sensing image super resolution method with multi-degradation

In current super-resolution (SR) research, blind SR models capable of handling multiple degradations have attracted significant attention. Inspired by variational autoencoders (VAEs) that model data distributions through latent representations, this paper proposes a VAE framework for unsupervised remote sensing image (RSI) SR. VAEs excel at learning rich latent representations, modeling probabilistic distributions of input data and unsupervised learning, making them inherently well-suited to real-world blind SR scenarios. The proposed framework consists of an encoder that maps low-resolution (LR) images into a latent space and a decoder that reconstructs super-resolved images from the latent representations. To enhance latent modeling, an alternating optimization strategy is implemented for training the encoder and decoder. Furthermore, a comprehensive loss function and a latent coding regularization strategy are designed to constrain latent representations while maintaining image domain consistency. Experimental results demonstrate that on synthetic data, our method achieves favorable performance in both visual quality and quantitative metrics. It also demonstrates competitively performance compared to supervised methods, particularly in 4 × and 8 × SR tasks. Additionally, evaluations on Jilin-1 satellite RSIs further validate the effectiveness of our approach. • The unsupervised SR method handles multi-degradation with satisfying performance. • The encoder and decoder are trained alternately. • The decoder decodes latent code into SR image with fine details. • Constraining latent code with comprehensive loss improves the SR results. • Regularization strategy improves SR performance on large ratios 4 and 8.