Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two…

• FD
  Fundação de Amparo à Pesquisa do Estado de São Paulo

  Award: 82172033,U19B2031,61971369,52105126,82272071,62271430