Image enhancement method of underground low illumination in coal mine based on improved CycleGAN

Aiming at the defects of low illumination，color distortion and serious loss of detail features in the images collected by the underground monitoring device，a image enhancement algorithm of underground low illumination in coal mine based on the improved CycleGAN network was proposed．First，aiming at the difficulty of acquiring underground paired image data，a cyclic image enhancement framework was built based on the cyclic generation antagonism network to realize unsupervised training of the model．Then，based on the image decomposition architecture of CSDNet，a dual-branch estimation network integrating the space-channel attention module CBAM was designed to estimate the illumination and reflection components of the image in parallel，and a multi-scale feature decomposition mechanism was established between the two branch networks，so as to avoid color distortion and retain a large amount of detail information while greatly improving the brightness．The global-local discriminator was used to adjust the brightness，improve the uneven brightness，and avoid over-exposure and shadow of the local area of the image．Experiment results show that compared with the comparison algorithm RetinexNet，LLNet，MBLLEN，EnlightenGAN and CSDNet，the performances of the proposed algorithm on the objective quality indicators PSNR (peak signal to noise ratio)，SSIM (structural similarity)，IFC (information fidelity criterion) and VIF (visual information fidelity) are improved by 11.787%，8.256%，9.658% and 8.654%，respectively，and is superior to the comparison algorithm in the subjective analysis of human vision，which proves that the algorithm can effectively improve the visual effect of underground low illumination images. © 2023 Huazhong University of Science and Technology. All rights reserved.