  Two difficulties here make low-light image enhancement a challenging task;
firstly, it needs to consider not only luminance restoration but also image
contrast, image denoising and color distortion issues simultaneously. Second,
the effectiveness of existing low-light enhancement methods depends on paired
or unpaired training data with poor generalization performance.
  To solve these difficult problems, we propose in this paper a new
learning-based Retinex decomposition of zero-shot low-light enhancement method,
called ZERRINNet. To this end, we first designed the N-Net network, together
with the noise loss term, to be used for denoising the original low-light image
by estimating the noise of the low-light image. Moreover, RI-Net is used to
estimate the reflection component and illumination component, and in order to
solve the color distortion and contrast, we use the texture loss term and
segmented smoothing loss to constrain the reflection component and illumination
component. Finally, our method is a zero-reference enhancement method that is
not affected by the training data of paired and unpaired datasets, so our
generalization performance is greatly improved, and in the paper, we have
effectively validated it with a homemade real-life low-light dataset and
additionally with advanced vision tasks, such as face detection, target
recognition, and instance segmentation. We conducted comparative experiments on
a large number of public datasets and the results show that the performance of
our method is competitive compared to the current state-of-the-art methods. The
code is available at:https://github.com/liwenchao0615/ZERRINNet
