Edge-based Denoising Image Compression
This work addresses image compression challenges for applications requiring high fidelity, such as transmission or noisy environments, but appears incremental as it builds on existing generative and diffusion methods.
The paper tackles the problem of diminished sharpness and quality in deep learning-based image compression by proposing a model that incorporates denoising with diffusion models and leverages edge and depth information from latent space, achieving superior or comparable results in image quality and compression efficiency.
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.