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Optical remote sensing (RS) satellites perform imaging in the visible and infrared electromagnetic spectrum to collect data and analyze information on the optical characteristics of the objects of interest. However, optical RS is sensitive to illumination and atmospheric conditions, especially clouds, and multiple acquisitions are typically required to obtain an image of sufficient quality. To accurately reproduce surface information that has been contaminated by clouds, this work proposes a generative adversarial network (GAN)-based cloud removal framework using a distortion coding network combined with compound loss functions (DC-GAN-CL). A novel generator embedded with distortion coding and feature refinement mechanisms is applied to focus on cloudy regions and enhance the transmission of optical information. In addition, to achieve feature and pixel consistency, both coherent semantics and local adaptive reconstruction factors are considered in our loss functions. Extensive numerical evaluations on RICE1, RICE2, and Paris datasets are performed to validate the good performance achieved by the proposed DC-GAN-CL in both peak signal-to-noise ratio (PSNR) and visual perception. This system can thus restore images to obtain similar quality to cloud-free reference images, in a dynamic range of over 30 dB. The restoration effect on the coherence of image semantics produced by this technique is competitive compared with other methods. [ABSTRACT FROM AUTHOR]