Single low-light image brightening using learning-based intensity mapping

Inspired by image-to-curve transformation and multi-exposure fusion, in this paper, we have developed a new method to treat the low light image enhancement tasks as an extended problem with multiple virtual exposures by a non-linear intensity mapping function. Considering that existing image-to-curve methods have difficulty in obtaining the desired detail and brightness recovery in any one iteration without relying on any ground truth, we propose a virtual multi-exposure fusion strategy to merge the outputs from these different iterations. Specifically, a simple is trained to learn a pixel-wise intensity mapping function and accordingly adjust a given image multiple times. Then the results of all iterations are retained together with the original input image for fusion via a WGIF-based Multi-scale pyramid to obtain a final enhanced output. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances.