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It is suggested that low-light image enhancement realizes one-to-many mappingsince we have different definitions of NORMAL-light given application scenariosor users' aesthetic. However, most existing methods ignore subjectivity of thetask, and simply produce one result with fixed brightness. This paper proposesa neural network for multi-level low-light image enhancement, which isuser-friendly to meet various requirements by selecting different images asbrightness reference. Inspired by style transfer, our method decomposes animage into two low-coupling feature components in the latent space, whichallows the concatenation feasibility of the content components from low-lightimages and the luminance components from reference images. In such a way, thenetwork learns to extract scene-invariant and brightness-specific informationfrom a set of image pairs instead of learning brightness differences. Moreover,information except for the brightness is preserved to the greatest extent toalleviate color distortion. Extensive results show strong capacity andsuperiority of our network against existing methods.

Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References