NAI$_2$: Learning Noise-Aware Illumination-Interpolator for Unsupervised Low-Light Image Enhancement

arXiv:2305.10223v3 Announce Type: replace
Abstract: Contemporary Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast, achieving commendable results on specific datasets. Nevertheless, these approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. Insufficient constraints on complex pixel-wise mapping learning lead to overfitting to specific types of noise and artifacts associated with low-light conditions, reducing effectiveness in variable lighting scenarios. To this end, we first propose a method for …

abstract arxiv challenges constraints contrast cs.cv cs.mm datasets diverse dynamic image light low mapping noise pixel results type unsupervised wise