Lang Yi-Zheng, Qian Yun-Sheng, Kong Xiang-Yu, Zhang Jing-Zhi
Appl Opt. 2022 Jul 20;61(21):6339-6348. doi: 10.1364/AO.457726.
Tone mapping operators (TMOs) aim to adjust high dynamic range (HDR) images to low dynamic range (LDR) ones so that they can be displayed on conventional devices with visual information retained. Nonetheless, existing TMOs can successfully tone-map only limited types of HDR images, and the parameters need to be manually adjusted to yield the best subjective-quality tone-mapped outputs. To cope with the aforementioned issues, an adaptive parameter-free and scene-adaptive TMO for dynamic range adjusting and detail enhancing is proposed to yield a high-resolution and high-subjective-quality tone-mapped output. This method is based on detail/base layer decomposition to decompose the input HDR image into coarse detail, fine detail, and base images. After that, we adopt different strategies to process each layer to adjust the overall brightness and contrast and to retain as much scene information. Finally, a new method, to the best of our knowledge, is proposed for visualization to generate a sequence of artificial images to adjust the brightness. Experiments with numerous HDR images and state-of-the-art TMOs are conducted; the results demonstrate that the proposed method consistently produces better quality tone-mapped images than the state-of-the-art methods.
色调映射算子（TMO）旨在将高动态范围（HDR）图像调整为低动态范围（LDR）图像，以便它们能够在传统设备上显示并保留视觉信息。然而，现有的TMO只能成功地对有限类型的HDR图像进行色调映射，并且需要手动调整参数才能产生主观质量最佳的色调映射输出。为了解决上述问题，提出了一种用于动态范围调整和细节增强的自适应无参数且场景自适应的TMO，以产生高分辨率和高主观质量的色调映射输出。该方法基于细节/基础层分解，将输入的HDR图像分解为粗细节、细细节和基础图像。之后，我们采用不同的策略处理每一层，以调整整体亮度和对比度，并保留尽可能多的场景信息。最后，据我们所知，提出了一种新的可视化方法，用于生成一系列人工图像来调整亮度。对大量HDR图像和最先进的TMO进行了实验；结果表明，所提出的方法始终能产生比最先进方法质量更好的色调映射图像。
