Unsupervised Low-light Image Enhancement Model with Adaptive Noise Suppression and Detail Preservation
The visual quality of images taken under low-light environment is usually low,due to many factors such as low light-ness and imaging noise.Current low-light image enhancement methods have a common limitation that they only focus on impro-ving lightness condition and suppressing noise,but neglect to preserve image details.To solve this problem,an unsupervised low light image enhancement method is proposed in this paper,aiming to improve the visibility and preserve the fidelity of an image with good efficiency.The model consists of two stages,i.e.,low-light enhancement and noise suppression.In the first stage,an unsupervised image decomposition module and a lightness enhancement module are constructed to achieve the goal of improving visibility.In the second stage,under the guidance of the illumination distribution of an image,we synthesize pairwise training data and train the denoising network to depress the imaging noise from the originally-dim regions and preserve the image details of the originally-bright regions.Compared with other methods,experimental results show that our method achieves better balance be-tween the goals of visibility improvement and fidelity preservation.In addition,our method can be attractive in real-world applica-tions,as it does not need to collect bright-dim image pairs,and it has small model size and fast calculation speed.
万方数据
维普
