A hierarchically sampling global sparse transformer in data stream mining for lightweight image restoration

With the rapid development of information technology, mining valuable information from multi-source data stream is essential for redundant data, particularly in image processing; the image is degraded when the image sensor acquires information. Recently, transformer has been applied to the image restoration (IR) and shown significant performance. However, its computational complexity grows quadratically with increasing spatial resolution, especially in IR tasks to obtain long-range dependencies between global elements through attention computation. To resolve this problem, we present a novel hierarchical sparse transformer (HST) network with two key strategies. Firstly, a coordinating local and global information mapping mechanism is proposed to perceive and feedback image texture information effectively. Secondly, we propose a global sparse sampler that reduces the computational complexity of feature maps while effectively capturing the association information of global pixels. We have conducted numerous experiments to verify the single/double layer structure and sampling method by analyzing computational cost and parameters. Experimental results on image deraining and motion deblurring show that the proposed HST performs better in recovering details compared to the baseline methods, achieving an average improvement of 1.10 dB PSNR on five image deraining datasets and excellent detail reconstruction performance in visualization.