Objective Low-light images can be obtained due to either low-light conditions or shooting techniques. In orde to solve such problems as low contrast high noise and color distortion of images a convolutional neural network enhancement model RetKIND was proposed which included a decomposition network brightness adjustment network and noise reduction network. Methods With the help of the residual block RB and skip connection the noise generated by the decomposition network during decomposition was effectively suppressed. The noise reduction network was constructed by integrating U-Net architecture dilated convolution and EBAM efficient attention mechanism. The dilated convolution was used to enlarge the receptive field to extract more image information and EBAM was utilized to extract details texture color and other features of the reflection image in channel and space to achieve image denoising. The brightness adjustment network was composed of a UC module self-designed module in the brightness adjustment network and traditional convolution which aimed to reduce the detail loss of light images and improve the contrast of the light component. Finally the denoised reflection component and the enhanced light component were fused to obtain the normal illumination image. Results Simulation results showed that on the dataset LOL compared with R2RNet the values of FPSNR and FSSIM increased by 6. 2% and 4. 2% respectively compared with URetinex-Net the values of FPSNR and FSSIM of Ret-KIND increased by 5. 9% and 1. 2% respectively compared with DEANet the values of FPSNR and FSSIM of RetKIND increased by 2. 9% and 1. 1% respectively. Conclusion The Ret-KIND model can not only improve image brightness but also reduce image noise which helps to promote the application of the low-light image enhancement model to the field of target detection.