Abstract
Image acquisition and restoration have always been an indispensable part of industrial production. Due to the harsh environment in which industrial cameras are used and problems such as network transmission loss, the images captured are not high definition enough, making image restoration particularly important. Existing image restoration methods have a large number of parameters and time-consuming training, resulting in inefficiency. In this paper, we propose DAResNet for the problem of balancing model size and image restoration performance to improve image restoration while reducing the number of parameters. By analyzing the characteristics of industrial blurred images and combining the features of the proposed model, we propose the Half-Channel Attention Mechanism (HCAM) while constructing the Half-Channel Attention Instance Normalization Block (HCAIN Block) and the Double-Layer Residual Block (DARes Block). Meanwhile, we establish the Industrial Blurred Image Dataset (IBID). Finally, four state-of-the-arts (SOTAs) are selected for comparison on IBID, and DAResNet achieves excellent results.
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