LLFlowGAN:a low-light image enhancement method for constraining invertible flow in a generative adversarial manner
Objective Low-light images are produced by imaging devices that cannot capture sufficient light due to unavoid-able environmental or technical limitations(such as nighttime,backlight,and underexposure).Such images usually have the characteristics of low brightness,low contrast,narrow grayscale range,color distortion,and strong noise,which almost need more information.Low-light images containing these problems do not meet human beings'visual requirements and directly limit the role of the subsequent advanced visual system.The low-light image enhancement task is an ill-posed prob-lem because the low-light image loss of illumination information,that is,a low-light image may correspond to countless normal-light images.Low-light image enhancement should be regarded as selecting the most suitable solution from all pos-sible outputs.Most existing reconstruction methods rely on pixel-level reconstruction algorithms that aim to learn a deter-ministic mapping between low-light inputs and normal-light images.They provide a normal-light result for a low-light image rather than modeling complex lighting distributions,which usually result in inappropriate brightness and noise.Further-more,most existing image generation methods use only one(explicit or implicit)generative model,which limits flexibility and efficiency.Flow models have recently demonstrated promising results for low-level vision tasks.This paper improves a hybrid explicit-implicit generative model,which can flexibly and efficiently reconstruct normal-light images with satisfied lighting,cleanliness,and realism from degraded inputs.The model alleviates the fuzzy details and singularity problems produced by explicit or implicit generative modeling.Method This paper proposes a low-light image enhancement network with a hybrid explicit(Flow)and implicit generative adversarial network(GAN),named LLFlowGAN that contains three parts:conditional encoder,flow generation network,and discriminator.Flow generation networks operate at multiple scales conditioned on encoded information from low-light input.First,a residual attention conditional encoder is designed to process low-light input,calculate low-light color maps,and extract rich features to reduce the color deviation of gener-ated images.Due to the flexibility of the flow model,the conditional encoder mainly consists of several residual blocks plus efficient stacking of channel attention modules.Then,the features extracted by the encoder are used as conditional prior to the generative flow model.Moreover,the flow model learns to map the high-dimensional random variables obeying the nor-mal exposure image distribution into a bidirectional mapping with simple tractable latent variables(Gaussian distribution).By simulating the conditional distribution of normal exposure images,the model allows the sampling of multiple normal exposure results to generate diverse samples.Finally,the GAN-based discriminator provides constraints for the model and improves the detailed information of the image in the reverse mapping.Because the model learns a bidirectional mapping relationship,both mapping directions can be regarded as constrained by the loss function,providing the network stability and resistance to mode collapse.Result The proposed algorithm in this paper is validated using experiments on two data-sets,namely,Low-Light(LOL)dataset and MIT-Adobe FiveK dataset,to verify its effectiveness.Quantitative evaluation metrics include peak signal-to-noise ratio(PSNR),structural similarity index measure(SSIM),learned perceptual image patch similarity(LPIPS),and natural image quality evaluator(NIQE).Our model is compared with 18 saliency models in the LOL dataset,including the traditional supervised and unsupervised deep learning methods including state-of-the-art methods in this field.Compared with the model with the second-best performance,our method improves the PSNR value by 0.84 dB and reduces the LPIPS value(the smaller,the better)by 0.02.SSIM obtains the second-best value,decreases by 0.01,and NIQE decreases by 1.05.Saliency maps of each method are also provided for comparison.Our method bet-ter preserves rich detail and color information while enhancing image brightness,where artifacts are rarely observed,achieving better perceptual quality.In the MIT-Adobe FiveK dataset,the five most advanced methods are compared.Com-pared with the model with the second-best performance,the PSNR value increases by 0.58 dB,and the SSIM value is also tied for first place.In addition,a series of ablation experiments and cross-dataset tests in the LOL dataset are conducted to verify the effectiveness of each algorithm module.Experimental results prove our proposed algorithm improves the effect of low-light image enhancement.Conclusion In this paper,a hybrid explicit-implicit generative model is proposed.The model inherits the flow-based explicit generative model,which can accurately complete the free conversion between the natural image space and a simple Gaussian distribution and flexibly generate diverse samples.The adversarial training strategy is further used to improve the detailed information of the generated image,enrich the saturation,and reduce the color distortion.The proposed approach can achieve competitive performance compared with representative state-of-the-art low-light image enhancement methods.
low-light images enhancementflow modelgenerative adversarial network(GAN)bidirectional mappingcomplex illumination distribution
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