Single Frame Deblurring with Laplacian Filters
This addresses deblurring for image processing applications, but appears incremental as it builds on existing architectures like Residual Dense Networks.
The paper tackles the ill-posed problem of blind single image deblurring by proposing a solution using Laplacian filters, showing significant improvement in image quality compared to state-of-the-art DNN methods on a benchmark dataset.
Blind single image deblurring has been a challenge over many decades due to the ill-posed nature of the problem. In this paper, we propose a single-frame blind deblurring solution with the aid of Laplacian filters. Utilized Residual Dense Network has proven its strengths in superresolution task, thus we selected it as a baseline architecture. We evaluated the proposed solution with state-of-art DNN methods on a benchmark dataset. The proposed method shows significant improvement in image quality measured objectively and subjectively.