Noisy images are a challenge to image compression algorithms due to the
inherent difficulty of compressing noise. As noise cannot easily be discerned
from image details, such as high-frequency signals, its presence leads to extra
bits needed for compression. Since the emerging learned image compression
paradigm enables end-to-end optimization of codecs, recent efforts were made to
integrate denoising into the compression model, relying on clean image features
to guide denoising. However, these methods exhibit suboptimal performance under
high noise levels, lacking the capability to generalize across diverse noise
types. In this paper, we propose a novel method integrating a multi-scale
denoiser comprising of Self Organizing Operational Neural Networks, for joint
image compression and denoising. We employ contrastive learning to boost the
network ability to differentiate noise from high frequency signal components,
by emphasizing the correlation between noisy and clean counterparts.
Experimental results demonstrate the effectiveness of the proposed method both
in rate-distortion performance, and codec speed, outperforming the current
state-of-the-art.

本文提出了一种集成多尺度去噪器的新方法，用于联合图像压缩和去噪。通过强调噪声与干净图像之间的相关性，采用对比学习的方式来提高网络区分噪声与高频信号的能力，实验证明该方法在失真性能和编解码速度方面优于当前的最新技术水平。