Riesz-Quincunx-UNet Variational Auto-Encoder for Unsupervised Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. Most traditional convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. The U-Net architecture avoids this information loss by introducing skip-connections that allow to reconstruct lost information. Leveraging this property of the U-Net, this study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (commonly used to reduce blur in medical images) inside the U-Net to reduce noise in satellite images and their time-series. Combining both approaches, we introduce a hybrid Riesz-Quincunx-UNet Variational Auto-Encoder (RQUNet-VAE) scheme for image and time series decomposition used to reduce noise in satellite imagery. By including denoising capabilities directly inside the UNet architecture, we hypothesize that our RQUNet-VAE may improve downstream image processing tasks that use the traditional U-Net architecture. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE is effective at reducing noise in satellite imagery yielding results similar to other state-of-the-art noise reduction methods. We further show that our RQUNet-VAE outperforms the U-Net architecture specifically in cases where images exhibit high levels of noise. We show this result in two down-stream applications for multi-band satellite images, including image time-series decomposition and image segmentation.