Regional Weighted Generative Adversarial Network for LDR to HDR Image Conversion

Jung, Sung‐Woon; Son, Dong-Min; Kwon, Hyunwook; Lee, Sung Hak

doi:10.1109/icaiic48513.2020.9064984

Cited by 4 publications

(3 citation statements)

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“…EV GAN uses image dataset with different exposure values [17,18]. This work trains the model to transform dark images into region-specific brightness values by inputting increasing EVs as EV-3, EV-4, and EV-5.…”

Section: Exposure Value Ganmentioning

confidence: 99%

“…However, the case of HDR image tone mapping is not straightforward. The conventional CycleGAN trains on the RGB channel [17,18], and thereby distorts the color and degrades the image quality. Therefore, correction is required when using a conventional GAN module, resulting in difficulty in HDR image generation.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, we propose the use of a regional fusion training (RFT) method designed for providing a difference in restoration speed according to the brightness of a region [17,18]. The restoration speed is the degree of improvement in the local bright and dark areas of a training image.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset

Jung¹,

Kwon²,

Lee³

2021

Applied Sciences

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High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.

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Section: Exposure Value Ganmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%