Edge-based Denoising Image Compression

TL;DR

This paper introduces a novel edge-based denoising image compression method using diffusion models, improving reconstruction quality and robustness against noise and partial data loss.

Contribution

It proposes a new compression framework that integrates denoising diffusion models with edge preservation to enhance image fidelity and robustness.

Findings

Achieves superior image quality compared to existing models.

Effectively handles partial image loss and noise.

Demonstrates improved compression efficiency.

Abstract

In recent years, deep learning-based image compression, particularly through generative models, has emerged as a pivotal area of research. Despite significant advancements, challenges such as diminished sharpness and quality in reconstructed images, learning inefficiencies due to mode collapse, and data loss during transmission persist. To address these issues, we propose a novel compression model that incorporates a denoising step with diffusion models, significantly enhancing image reconstruction fidelity by sub-information(e.g., edge and depth) from leveraging latent space. Empirical experiments demonstrate that our model achieves superior or comparable results in terms of image quality and compression efficiency when measured against the existing models. Notably, our model excels in scenarios of partial image loss or excessive noise by introducing an edge estimation network to preserve the integrity of reconstructed images, offering a robust solution to the current limitations of image compression.