and learn to directly generate its high-resolution (HR) version. In contrast to directly producing a latent HR image, recent SR networks [@VDSR; @tai2017memnet; @tai2017image; @hui2018fast] employ the residual learning framework [@He2016] to learn the high-frequency image detail, which is later added to the input LR image to produce the final super-resolved result. Other networks designed to perform SR include recursive learning [@kim2016deeply; @han2018image; @ahn2018fast], progressive reconstruction [@wang2015deep; @Lai2017], dense connections [@tong2017image; @wang2018esrgan; @zhang2020residual], attention mechanisms [@RCAN; @dai2019second; @zhang2019residual], multi-branch learning [@Lai2017; @EDSR; @dahl2017pixel; @li2018multi], and generative adversarial models [@wang2018esrgan; @park2018srfeat; @sajjadi2017enhancenet; @SRResNet]. **Image enhancement.** Oftentimes, cameras provide images that are less vivid and lack contrast. A number of factors contribute to the low quality of images, including unsuitable lighting conditions and physical limitations of camera devices. For image enhancement, histogram equalization is the most commonly used approach. However, it frequently produces under-enhanced or over-enhanced images. Motivated by the Retinex theory [@land1977retinex], several enhancement algorithms mimicking human vision have been proposed in the literature [@bertalmio2007; @palma2008perceptually; @jobson1997multiscale; @rizzi2004retinex]. Recently, CNNs have been successfully applied to general, as well as low-light, image enhancement problems. Notable works employ Retinex-inspired networks [@Shen2017; @wei2018deep; @zhang2019kindling], encoder-decoder networks [@chen2018encoder; @Lore2017; @ren2019low], and generative adversarial networks [@chen2018deep; 