# Transformer-based Learned Image Compression for Joint Decoding and Denoising

arXiv: https://arxiv.org/abs/2402.12888 · HTML: https://mlsanity.com/paper/2402.12888/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2024.

## Built on by (most cited first, 2 of 2)

- [All-in-One Image Compression and Restoration](https://mlsanity.com/paper/2502.03649/) (2025)
- [End-to-End Semantic Preservation in Text-Aware Image Compression Systems](https://mlsanity.com/paper/2503.19495/) (2025)

## Abstract (arXiv)

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.