Joint End-to-End Image Compression and Denoising: Leveraging Contrastive
  Learning and Multi-Scale Self-ONNs
Yuxin Xie Li Yu Farhad Pakdaman Moncef Gabbouj
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.