Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation

Hanbyol Jang,Kihun Bang,Jinseong Jang,Dosik Hwang

doi:10.1109/access.2020.2975857

Hanbyol Jang, Kihun Bang + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.2975857

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Abstract

In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye. Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images. The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks. In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR–HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images. The novel learning technique is called cumulative histogram-based learning and color difference learning. The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images.

Highlights

In photographs, the dynamic range refers to the luminance range from the darkest region to the brightest region
The test error graph of the trained model shows the effect of transfer learning. It shows (a) a model trained with 450 low dynamic range (LDR) and true high dynamic range (HDR) pair images, (b) a model trained with 8000 LDR and pseudo-HDR pair images, and (c) a model trained with 450 LDR and true HDR pair images from pretrained weights on 8000 LDR and pseudo-HDR pair images
All cases were tested with 450 LDR and true HDR pair images with 5-fold cross-validation [22]

Summary

Introduction

The dynamic range refers to the luminance range from the darkest region to the brightest region. Most dynamic ranges commonly used in current digital displays are below 300 cd/m2. This is referred to as a low dynamic range (LDR). If the expressed dynamic range is narrow, the differences in brightness that human eyes can distinguish may be displayed as a constant level of brightness. Wide dynamic range scenes, such as sunrises or sunsets, cannot properly be captured or displayed using LDR settings. The current LDR technologies are insufficient to attain the levels of perception made possible by the human eyes. Techniques that can expand the dynamic range of images as wide as the cognitive range of the human eye are necessary

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Conclusion