Improving the deblurring method of D2Net network for infrared videos

Abstract

When facing motion and complex environmental conditions, infrared videos captured by thermal imaging devices often suffer from blurring, leading to unclear or missing details and positional information about the targets. To improve this problem, this work proposes an improved deblurring method suitable for infrared videos based on a deep learning-based deblurring network originally designed for visible light images. This method is built upon the D2Net network by introducing a spatial and channel reconstruction convolution for feature redundancy, enhancing the network’s capability for image feature learning. In terms of the encoder-decoder module, a triple attention mechanism and fast Fourier transform are introduced to further improve the network’s deblurring performance. Through ablative experiments on infrared datasets, the results demonstrate a significant improvement in deblurring performance compared to the original D2Net. Specifically, the improved network achieved a 1.42 dB increase in peak signal-to-noise ratio and a 0.02 dB increase in structural similarity compared to the original network. In summary, this paper achieves promising results in infrared video deblurring tasks, demonstrating the effectiveness of the proposed method.