JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.

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.

Convolutional Neural Network (CNN) has achieved great success in visual applications. In the field of image classification, researchers usually customize CNN models to meet the needs of different real-world applications. It consumes a lot of human labor and computing resources but only achieves slight performance improvement. Besides, some recent works try to integrate prior knowledge into classic CNN models to improve their accuracy, but they are usually only effective for special applications. In this paper, we propose a gradient-guided direction-aware convolution (GDC) block. It can be used to replace low-level convolutions of existing CNN without changing the off-the-shelf architecture. The gradient priors provide object shapes that CNN’s low-level convolution requires. And the direction-aware mechanism expands the receptive field size. This scheme is a trade-off between model size and model accuracy. Experimental results show it can moderately reduce the size of any CNN models while enhancing their performance.