Aiming at the problems existing in current image dehazing methods, such as dark color of output image, loss of scene information and incomplete dehazing, an end-to-end image dehazing method based on attention mechanism is proposed.Firstly, the channel attention mechanism is embedded into the inception network, and the shallow features are extracted from the fused network.Then, the deep image information is learned by multi-scale convolution and residual dense connection blocks, the depth and shallow feature fusion is realized by skip connection.Finally, it is returned to the pixel scale coefficient matrix through a single convolution layer, and a fog-free image is generated according to the improved atmospheric scattering model.Based on the Mean Square Error (MSE), the fidelity loss function is designed as a constraint in the network model.The experimental results on RESIDE dataset show that the Peak Signal-to-Noise Ratio(PSNR), Structure Similarity (SSIM), Learning Perceptual Image Patch Similarity (LPIPS) and CIEDE2000 of the proposed method reach 32.545, 0.970, 0.026 and 2.711 respectively.The method shows good effect, the output image is dehazed thoroughly and the color fidelity is high;It effectively avoids the loss of detail information in the existing methods.