Low-light image enhancement algorithm based on illumination and scene texture attention map
Objective Owing to the lack of sufficient environmental light,images captured from low-light scenes often suf-fer from several kinds of degradations,such as low visibility,low contrast,intensive noise,and color distortion.Such deg-radations will not only lower the visual perception quality of the images but also reduce the performance of the subsequent middle-and high-level vision tasks,such as object detection and recognition,semantic segmentation,and automatic driv-ing.Therefore,the images taken under low-light conditions should be enhanced to meet subsequent utilization.Low-light image enhancement is one of the most important low-level vision tasks,which aims at improving the illumination and recov-ering image details of dark regions with lighting noise and has been intensively studied.Many impressive traditional meth-ods and deep learning-based methods have been proposed.The methods achieved by traditional image processing tech-niques mainly include value mapping(such as histogram equalization and gamma correction)and model-based methods(such as Retinex model and atmospheric scattering model).However,they only improve image quality from a single per-spective,such as contrast or dynamic range,and neglect such degradations as noise and scene detail recovery.On the con-trary,with the great development of deep neural networks in low-level computer vision,deep learning-based methods can simultaneously optimize the enhancement results from multiple perspectives,such as brightness,color,and contrast.Thus,the enhancement performance is significantly improved.Although significant progress has been achieved,the exist-ing deep learning-based enhancement methods have drawbacks,such as underenhancement,overenhancement,and color distortion in local areas,and the enhanced results are inconsistent with the visual characteristics of human eyes.In addi-tion,given the high distortion degree of extremely low-light images,recovering scene details and suppressing noise amplifi-cation during enhancement are usually difficult.Therefore,increased attention should be paid to low-light image enhance-ment methods.To this end,a low-light image enhancement algorithm based on illumination and scene texture attention map is proposed in this paper.Method First,unlike in normal-light images,the illumination intensity of RGB channels is obviously different in low-light images,leading to apparent color distortion.Color equalization processing is performed for low-light images to reduce the influence of color distortion on the estimation module of attention map.We implement color equalization using the illumination intensity of RGB channels estimated with the dark channel prior to make the light inten-sity of each channel similar.Second,considering that the minimum channel constraint map has the characteristics of noise suppression and texture prominence,we estimate the illumination and texture attention map of normal-exposure images on the basis of the minimum channel constraint map of low-light images and provide information guidance for the subsequent enhancement module.Thus,an attention map estimation module based on the U-Net architecture is proposed.Third,an enhancement module is developed to improve image quality from the perspectives of the whole image and local patches.In the global enhancement module,the estimated illumination and scene texture attention map is used to guide the illumina-tion adjustment and noise suppression.The attention mechanism can enable the network to allocate different attention to various brightness areas in low-light images during the training process to help the network focus on useful information effec-tively.The global enhanced result is divided into small patches to deal with the problems of underenhancement and overen-hancement in local areas to improve the results further.Result To verify the effectiveness of the proposed method,we com-pare it with six state-of-the-art enhancement methods,including two traditional methods:semi-decomposed decomposition(SDD)and plug-and-play Retinex model(PnPR),and four deep learning-based methods:EnlightenGAN,zero-reference deep curve estimation(Zero-DCE),Retinex-based deep unfolding network(URetinex-Net),and signal-to-noise-ratio aware low light image enhancement(SNR-aware).We use digital images from commercial cameras(DICM),low-light image enhancement(LIME),multi-exposure image fusion(MEF),and 9 other datasets to construct 178 low-light images for testing.These low-light images do not have normal-exposure image for reference.Quantitative and qualitative evalua-tions are performed.For the quantitative evaluation,natural image quality evaluator(NIQE),blind tone-mapped quality index(BTMQI),and no-reference image quality metric for contrast distortion(NIQMC)are used to assess image quality.NIQE examines the image with the designed natural image model.BTMQI evaluates image perception quality after tone mapping by analyzing the naturalness and structure.For NIQE and BTMQI,the lower the value is,the higher the natural quality of the image is.NIQMC evaluates image quality by calculating the contrast between the local properties and the related properties of the blocks in the image.The higher the score is,the better the image quality is.On the VV dataset,which is a challenging dataset,our method obtains the best results for the BTMQI and NIQMC indicators.Experiments on the 178 low-light images show that our method achieves suboptimal values for the BTMQI and NIQMC metrics,but the advantages of texture prominence and noise suppression are significant.Conclusion Experimental results indicate that the enhanced results by our method achieve expected visual effects in terms of brightness,contrast,and noise suppression.In addition,our method can realize expected enhancement results for extremely low-light images.
NETL
NSTL
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