  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.
