MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction

• URL: http://arxiv.org/abs/2103.12545v1
• Date: Sat, 20 Mar 2021 07:56:45 GMT
• Title: MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction
• Authors: Edwin Pan, Anthony Vento
• Abstract summary: Existing approaches for converting low dynamic range images to high dynamic range images are limited by the assumption that all conversions are governed by the same nonlinear mapping. We propose "Meta-Learning for HDR-Agnostic Image Reconstruction" (Meta), which applies meta-learning to the LDR-to-Model conversion problem using existing HDR datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Capturing scenes with a high dynamic range is crucial to reproducing images that appear similar to those seen by the human visual system. Despite progress in developing data-driven deep learning approaches for converting low dynamic range images to high dynamic range images, existing approaches are limited by the assumption that all conversions are governed by the same nonlinear mapping. To address this problem, we propose "Model-Agnostic Meta-Learning for HDR Image Reconstruction" (MetaHDR), which applies meta-learning to the LDR-to-HDR conversion problem using existing HDR datasets. Our key novelty is the reinterpretation of LDR-to-HDR conversion scenes as independently sampled tasks from a common LDR-to-HDR conversion task distribution. Naturally, we use a meta-learning framework that learns a set of meta-parameters which capture the common structure consistent across all LDR-to-HDR conversion tasks. Finally, we perform experimentation with MetaHDR to demonstrate its capacity to tackle challenging LDR-to-HDR image conversions. Code and pretrained models are available at https://github.com/edwin-pan/MetaHDR.

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