DeTNet: a projector deblurring method based on CNN and Triplet attention

Projection blurring and defocusing is a prevalent issue that can significantly degrade the quality and legibility of projected images and visual content. This paper introduces a novel method to address this problem through the development of a deblurring network based on convolution operation and Triplet attention (DeTNet). This dual-pronged design enables the network to effectively extract salient features related to out-of-focus blurring, while also capturing the crucial interdependencies and interactions across multiple feature dimensions. By modeling both the low-level blur characteristics as well as the higher-order feature correlations, the DeTNet is able to reconstruct sharper, more focused projection outputs. Through extensive experimental validation on the collected datasets, the effectiveness of the proposed approach is thoroughly demonstrated.