  Images captured under low-light scenarios often suffer from low quality.
Previous CNN-based deep learning methods often involve using Retinex theory.
Nevertheless, most of them cannot perform well in more complicated datasets
like LOL-v2 while consuming too much computational resources. Besides, some of
these methods require sophisticated training at different stages, making the
procedure even more time-consuming and tedious. In this paper, we propose a
more accurate, concise, and one-stage Retinex theory based framework, RSEND.
RSEND first divides the low-light image into the illumination map and
reflectance map, then captures the important details in the illumination map
and performs light enhancement. After this step, it refines the enhanced
gray-scale image and does element-wise matrix multiplication with the
reflectance map. By denoising the output it has from the previous step, it
obtains the final result. In all the steps, RSEND utilizes Squeeze and
Excitation network to better capture the details. Comprehensive quantitative
and qualitative experiments show that our Efficient Retinex model significantly
outperforms other CNN-based models, achieving a PSNR improvement ranging from
0.44 dB to 4.2 dB in different datasets and even outperforms transformer-based
models in the LOL-v2-real dataset.
