Toward Blind-Adaptive Remote Sensing Image Restoration
Maomei Liu, Lei Tang, Lijia Fan, Sheng Zhong, Hangzai Luo, Jinye Peng · IEEE Transactions on Geoscience and Remote Sensing · 2023
While deep convolutional neural networks (CNNs) have substantially boosted the performance of low-level vision tasks, they remain largely under-explored in CNN-based remote sensing image restoration. This paper studies the JPEG-LS compressed remote sensing image restoration that faces the following problems. It requires a trade-off in preserving local context information and expanding spatial receptive fields. It needs blind restoration while achieving flexible performance. To this end, we propose a blind-adaptive restoration network, called TBANet, that integrates three modules into an end-to-end network to remedy these problems separately. Specifically, we build a scale-invariant wise-skip ResNet as the baseline to extract more context information. We present a receptive field expansion module by using scale-wise convolution for removing banding artifacts. We design a blind-adaptive controller to provide a deterministic result meanwhile meeting the needs of the user’s preference. In experiments, we compare the restoration accuracy among our model and many different variants of restoration methods on our collected remote sensing image dataset. The proposed network achieves superior performance against state-of-the-art methods in terms of both quantitative metrics and visual quality. Code and models are available at: https://github.com/lmmhh/TBANet.