Exploiting the Kurtosis Concentration Property for Image quality improvement

Aniket Roy, Maitreya Suin, Anshul Shah, Ketul Shah, Jiang Liu, Rama Chellappa

Keywords: kurtosis concentration, diffusion model

Abstract:

Diffusion models have significantly advanced generative AI in terms of creating and editing naturalistic images.
However, improving the image quality of generated images is still of paramount interest.
In this context, we propose a generic kurtosis concentration (KC) loss, which can be readily applied to any standard diffusion model pipeline to improve image quality. Our motivation stems from the \emph{projected kurtosis concentration property} of natural images, which states that natural images have nearly constant kurtosis values across different band-pass versions of the image. To improve the image quality of generated images, we reduce the gap between the highest and lowest kurtosis values across the band-pass versions (e.g., Discrete Wavelet Transform (DWT)) of images. In addition, we also propose a novel condition-agnostic perceptual guidance strategy during inference to further improve the image quality. We validate the proposed approach for three diverse tasks, viz., (1) personalized few-shot finetuning using text guidance, (2) unconditional image generation, and (3) image super-resolution. Integrating the proposed KC loss and perceptual guidance has improved the perceptual quality across all these tasks in terms of FID, MUSIQ score, and user evaluation. Code is provided in appendix.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 13739