Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captured images. More recently, learning-based solutions have shown impressive results in image enhancement in general, and in image denoising in particular. In this paper, we review multiple novel solutions for image denoising in the compressed domain, by integrating denoising operations into the decoder of a learning-based compression method. The paper starts by explaining the advantages of such an approach from different points of view. We then describe the proposed solutions, including both blind and non-blind methods, comparing them to state of the art methods. Finally, conclusions are drawn from the obtained results, summarizing the advantages and drawbacks of each method.