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Due to limited camera capacities, digital images usually have a narrowerdynamic illumination range than real-world scene radiance. To resolve thisproblem, High Dynamic Range (HDR) reconstruction is proposed to recover thedynamic range to better represent real-world scenes. However, due to differentphysical imaging parameters, the tone-mapping functions between images and realradiance are highly diverse, which makes HDR reconstruction extremelychallenging. Existing solutions can not explicitly clarify a correspondingrelationship between the tone-mapping function and the generated HDR image, butthis relationship is vital when guiding the reconstruction of HDR images. Toaddress this problem, we propose a method to explicitly estimate the tonemapping function and its corresponding HDR image in one network. Firstly, basedon the characteristics of the tone mapping function, we construct a model by apolynomial to describe the trend of the tone curve. To fit this curve, we use alearnable network to estimate the coefficients of the polynomial. This curvewill be automatically adjusted according to the tone space of the Low DynamicRange (LDR) image, and reconstruct the real HDR image. Besides, since allcurrent datasets do not provide the corresponding relationship between the tonemapping function and the LDR image, we construct a new dataset with bothsynthetic and real images. Extensive experiments show that our methodgeneralizes well under different tone-mapping functions and achieves SOTAperformance.

High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation