Dynamic Scene Video Deblurring using Non-Local Attention
This addresses video deblurring for computer vision applications, with incremental improvements in method efficiency.
The paper tackled video deblurring by proposing a factorized spatio-temporal attention method that avoids alignment, resulting in superior performance and efficiency compared to existing techniques.
This paper tackles the challenging problem of video deblurring. Most of the existing works depend on implicit or explicit alignment for temporal information fusion which either increase the computational cost or result in suboptimal performance due to wrong alignment. In this study, we propose a factorized spatio-temporal attention to perform non-local operations across space and time to fully utilize the available information without depending on alignment. It shows superior performance compared to existing fusion techniques while being much efficient. Extensive experiments on multiple datasets demonstrate the superiority of our method.