改进循环生成对抗网络的低光照图像增强方法OA

Aiming at the limitations of existing low-light image enhancement methods in solving problems such as noise and color bias,a low-light image enhancement method based on improved loop generative adversarial network(low light generative adversarial network,LLGAN)is proposed.The method firstly proposes a multi-scale residual block for extract-ing image multi-scale features;at the same time,a special convolutional StarConv is used in order to efficiently learn non-linear features in low-dimensional space.Secondly,a feature aggregation module is used and a learnable hybrid attention mechanism is proposed to simultaneously denoise and enhance the image brightness.Then,the gating mechanism LGAG is utilized to reduce the information loss caused by hopping connections.Finally,a global-local discriminator is proposed to reduce the loss of details during the enhancement process.The results show that the proposed LLGAN model has signifi-cant effects in enhancing image saturation and reducing noise,and its PSNR,SSIM,and NIQE reach 22.321 5 dB,0.8635,and 3.796 8,respectively,which are excellent compared with the existing mainstream methods.