  This work introduces a Transformer-based image compression system. It has the
flexibility to switch between the standard image reconstruction and the
denoising reconstruction from a single compressed bitstream. Instead of
training separate decoders for these tasks, we incorporate two add-on modules
to adapt a pre-trained image decoder from performing the standard image
reconstruction to joint decoding and denoising. Our scheme adopts a two-pronged
approach. It features a latent refinement module to refine the latent
representation of a noisy input image for reconstructing a noise-free image.
Additionally, it incorporates an instance-specific prompt generator that adapts
the decoding process to improve on the latent refinement. Experimental results
show that our method achieves a similar level of denoising quality to training
a separate decoder for joint decoding and denoising at the expense of only a
modest increase in the decoder's model size and computational complexity.
