Due to limited camera capacities, digital images usually have a narrower dynamic illumination range than real-world scene radiance. To resolve this problem, High Dynamic Range (HDR) reconstruction is proposed to recover the dynamic range to better represent real-world scenes. However, due to different physical imaging parameters, the tone-mapping functions between images and real radiance are highly diverse, which makes HDR reconstruction extremely challenging. Existing solutions can not explicitly clarify a corresponding relationship between the tone-mapping function and the generated HDR image, but this relationship is vital when guiding the reconstruction of HDR images. To address this problem, we propose a method to explicitly estimate the tone mapping function and its corresponding HDR image in one network. Firstly, based on the characteristics of the tone mapping function, we construct a model by a polynomial to describe the trend of the tone curve. To fit this curve, we use a learnable network to estimate the coefficients of the polynomial. This curve will be automatically adjusted according to the tone space of the Low Dynamic Range (LDR) image, and reconstruct the real HDR image. Besides, since all current datasets do not provide the corresponding relationship between the tone mapping function and the LDR image, we construct a new dataset with both synthetic and real images. Extensive experiments show that our method generalizes well under different tone-mapping functions and achieves SOTA performance.

翻译：由于相机成像能力的限制，数字图像通常比真实场景辐射具有更窄的动态照明范围。为解决这一问题，高动态范围（HDR）重建被提出以恢复动态范围，从而更好地表示真实场景。然而，由于物理成像参数不同，图像与真实辐射之间的色调映射函数高度多样化，这使得HDR重建极具挑战性。现有方法无法明确阐明色调映射函数与生成HDR图像之间的对应关系，而这种关系在指导HDR图像重建时至关重要。为解决这一问题，我们提出了一种方法，在一个网络中显式估计色调映射函数及其对应的HDR图像。首先，基于色调映射函数的特性，我们构建了一个多项式模型来描述色调曲线的趋势。为拟合该曲线，我们使用一个可学习网络来估计多项式的系数。该曲线将根据低动态范围（LDR）图像的色调空间自动调整，并重建真实的HDR图像。此外，由于现有数据集均未提供色调映射函数与LDR图像之间的对应关系，我们构建了一个包含合成图像与真实图像的新数据集。大量实验表明，我们的方法在不同色调映射函数下均具有良好的泛化性能，并达到了最先进的水平（SOTA）表现。