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Most recent methods of deep image enhancement can be generally classifiedinto two types: decompose-and-enhance and illumination estimation-centric. Theformer is usually less efficient, and the latter is constrained by a strongassumption regarding image reflectance as the desired enhancement result. Toalleviate this constraint while retaining high efficiency, we propose a noveltrainable module that diversifies the conversion from the low-light image andillumination map to the enhanced image. It formulates image enhancement as acomparametric equation parameterized by a camera response function and anexposure compensation ratio. By incorporating this module in an illuminationestimation-centric DNN, our method improves the flexibility of deep imageenhancement, limits the computational burden to illumination estimation, andallows for fully unsupervised learning adaptable to the diverse demands ofdifferent tasks.

Deep Quantigraphic Image Enhancement via Comparametric Equations