In order to solve the problems of texture details loss, indistinguishable treatment of all channel and spatial feature information and poor deblurring effect in the process of image restoration, an image deblurring method based on dual task convolutional neural network is proposed. The image deblurring task is divided into deblurring sub task and high frequency detail restoration sub task. Firstly, a coding and decoding sub network model based on Residual Attention Module and Octave Convolution Residual Block is proposed, which is used in image deblurring sub task. Secondly, a high frequency detail recovery sub network model based on Double Residual Connection is proposed, which is used in high frequency detail recovery sub task. The two subnetworks are combined in parallel, and the average absolute error loss and structure loss are used to constrain the training direction to achieve image deblurring. The experimental results show that the proposed method has strong image restoration ability and rich detail texture, the peak signal-to-noise ratio (PSNR) is 32.427 6 dB, and the structure similarity(SSIM) is 0.947 0. Compared with the current advanced deblurring algorithm, it can effectively improve the image deblurring effect.