Deep reverse tone mapping

Author:

Endo Yuki1,Kanamori Yoshihiro1,Mitani Jun1

Affiliation:

1. University of Tsukuba

Abstract

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images ) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

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1. Reconstructing fisheye luminance maps with a two-step network from a single low dynamic range image;Automation in Construction;2024-04

2. Lightweight improved residual network for efficient inverse tone mapping;Multimedia Tools and Applications;2024-01-19

3. DEUNet: Dual-encoder UNet for simultaneous denosing and reconstruction of single HDR image;Computers & Graphics;2024-01

4. Single Image HDR Synthesis with Histogram Learning;Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications;2023-11-27

5. Single-image HDR Reconstruction based on Mask-aware Convolution;Proceedings of the 2023 7th International Conference on Advances in Image Processing;2023-11-17

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