Exposure Diffusion: HDR Image Generation by Consistent LDR denoising

• URL: http://arxiv.org/abs/2405.14304v1
• Date: Thu, 23 May 2024 08:24:22 GMT
• Title: Exposure Diffusion: HDR Image Generation by Consistent LDR denoising
• Authors: Mojtaba Bemana, Thomas Leimkühler, Karol Myszkowski, Hans-Peter Seidel, Tobias Ritschel,
• Abstract summary: We seek inspiration from the HDR image capture literature that traditionally fuses sets of LDR images, called "brackets", to produce a single HDR image. We operate multiple denoising processes to generate multiple LDR brackets that together form a valid HDR result.
• Score: 29.45922922270381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We demonstrate generating high-dynamic range (HDR) images using the concerted action of multiple black-box, pre-trained low-dynamic range (LDR) image diffusion models. Common diffusion models are not HDR as, first, there is no sufficiently large HDR image dataset available to re-train them, and second, even if it was, re-training such models is impossible for most compute budgets. Instead, we seek inspiration from the HDR image capture literature that traditionally fuses sets of LDR images, called "brackets", to produce a single HDR image. We operate multiple denoising processes to generate multiple LDR brackets that together form a valid HDR result. To this end, we introduce an exposure consistency term into the diffusion process to couple the brackets such that they agree across the exposure range they share. We demonstrate HDR versions of state-of-the-art unconditional and conditional as well as restoration-type (LDR2HDR) generative modeling.

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