Optical remote sensing imagery is at the core of many Earth observation activities. Many applications take use of the satellite data's regular, consistent, and global‐scale characteristics, such as farmland monitoring, climate change assessment, land‐cover, and land‐use categorization, and catastrophe assessment. Optical remote sensing images, on the other hand, are frequently impacted by clouds during the collection process, resulting in reduced image clarity, which impairs feature assessment and future usage, and heavy cloud blockage renders the surface information below totally useless. In this paper, We propose a soft attention recurrent neural module based on an encoder‐decoder network, which can solve the cloud occlusion problem. We also propose an adaptive padding convolution at the end of the decoder by taking into account the spatial information, which results in better declouding predictions, and our network achieves good results on the RICE1 and RICE2 data sets.

Clouds frequently affect optical remote sensing pictures throughout the gathering process, resulting in low-resolution images that affect judgment and subsequent use of ground data. Because of the thick cloud cover, the ground surface information below is entirely incorrect. This kind of end-to-end image problem should not be dismissed as a simple task of image inpainting or image translation. Therefore, this paper proposes a multi-head self-attention module based on the encoding–decoding generative adversarial network, considering the redundant information of the deep network, furthermore this paper introduces Ghost convolution to effectively solve the influence of redundant feature maps in the network on the increase of time consumption and parameters. The method in this paper can solve the problem of cloud occlusion. By considering spatial information, it can better complete the prediction of cloud removal. It can reduce the amount of network calculations and parameters while maintaining the effect. In addition, Feature Fusion Module is proposed to integrate high-level features with low-level features, so that the network can extract enough feature information and better supplement the details to complete the cloud removal. The method in this paper has achieved excellent results on the RICE1 and RICE2 datasets.

Because of the unique physical and chemical properties of water, obtaining high-quality underwater images directly is not an easy thing. Hence, recovery and enhancement are indispensable steps in underwater image processing and have therefore become research hotspots. Nevertheless, existing image-processing methods generally have high complexity and are difficult to deploy on underwater platforms with limited computing resources. To tackle this issue, this paper proposes a simple and effective baseline named UIR-Net that can recover and enhance underwater images simultaneously. This network uses a channel residual prior to extract the channel of the image to be recovered as a prior, combined with a gradient strategy to reduce parameters and training time to make the operation more lightweight. This method can improve the color performance while maintaining the style and spatial texture of the contents. Through experiments on three datasets (MSRB, MSIRB and UIEBD-Snow), we confirm that UIR-Net can recover clear underwater images from original images with large particle impurities and ocean light spots. Compared to other state-of-the-art methods, UIR-Net can recover underwater images at a similar or higher quality with a significantly lower number of parameters, which is valuable in real-world applications.