  Maritime images captured under low-light imaging condition easily suffer from
low visibility and unexpected noise, leading to negative effects on maritime
traffic supervision and management. To promote imaging performance, it is
necessary to restore the important visual information from degraded low-light
images. In this paper, we propose to enhance the low-light images through
regularized illumination optimization and deep noise suppression. In
particular, a hybrid regularized variational model, which combines L0-norm
gradient sparsity prior with structure-aware regularization, is presented to
refine the coarse illumination map originally estimated using Max-RGB. The
adaptive gamma correction method is then introduced to adjust the refined
illumination map. Based on the assumption of Retinex theory, a guided
filter-based detail boosting method is introduced to optimize the reflection
map. The adjusted illumination and optimized reflection maps are finally
combined to generate the enhanced maritime images. To suppress the effect of
unwanted noise on imaging performance, a deep learning-based blind denoising
framework is further introduced to promote the visual quality of enhanced
image. In particular, this framework is composed of two sub-networks, i.e.,
E-Net and D-Net adopted for noise level estimation and non-blind noise
reduction, respectively. The main benefit of our image enhancement method is
that it takes full advantage of the regularized illumination optimization and
deep blind denoising. Comprehensive experiments have been conducted on both
synthetic and realistic maritime images to compare our proposed method with
several state-of-the-art imaging methods. Experimental results have illustrated
its superior performance in terms of both quantitative and qualitative
evaluations.
