Abstract
Due to the cost limitation of camera sensors, images captured in low-light environments often suffer from low contrast and multiple types of noise. A number of algorithms have been proposed to improve contrast and suppress noise in the input low-light images. In this paper, a deep refinement network, LL-RefineNet, is built to learn from the synthetical dark and noisy training images, and perform image enhancement for natural low-light images in symmetric—forward and backward—pathways. The proposed network utilizes all the useful information from the down-sampling path to produce the high-resolution enhancement result, where global features captured from deeper layers are gradually refined using local features generated by earlier convolutions. We further design the training loss for mixed noise reduction. The experimental results show that the proposed LL-RefineNet outperforms the comparative methods both qualitatively and quantitatively with fast processing speed on both synthetic and natural low-light image datasets.
Highlights
Video surveillance is of vital importance for various automated decision-making tasks including security application and medical care [1,2,3]
Guo et al [37] proposed a low-light image enhancement (LIME) method, which estimated the illumination of each pixel and utilized illumination map estimation
In contrast to most traditional image enhancement methods which only deal with the single type of Gaussian noise using L2 loss, we further introduce L1 loss for sparse impulse noise, which is closer to the enhancement of real low-light images
Summary
Video surveillance is of vital importance for various automated decision-making tasks including security application and medical care [1,2,3]. It is very important to design effective algorithms to process and enhance the low-light images in video surveillance systems. Methods based on deep learning framework have drawn enormous interest due to their effectiveness [9,10,11,12,13,14,15,16,17,18,19,20,21] These approaches are superior to previous traditional algorithms in multiple areas of computer vision, including object recognition [22], object detection [23], semantic segmentation [3], super resolution [24], and image denoising [25]. The content of the paper is organized as: related work is reviewed, which introduces recent algorithms for low-light image improvement and deep learning based image processing tasks.
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