BLIND IMAGE DEBLURRING BASED ON DUAL ATTENTION NETWORK AND 2D BLUR KERNEL ESTIMATION

In the problem of image deblurring, the restoration of details in severely blurred images has always been difficult. In this paper, we focus on effectively eliminating the ringing artifact and wrinkles that appear after deburring, and propose a novel blind debluring method based on dual attention deep image prior (DADIP) network and 2-dimensional (2D) blur kernel estimation with convolutional neural network (CNN). In the DADIP network, the dual attention mechanism is firstly combined with squeeze and excitation network (SENet), which greatly improves the restoration effect of image details. More importantly, the 2D blur kernel estimation approach via CNN is developed to suppress the ringing artifact of the image, which significantly outperforms previous fully connected network based methods. Experiments show that our deblurring approach achieves superior performance compared with most existing methods.