Deep salient video deblurring framework using GAN
Abstract
Deblurring has been a major challenge in image restoration tasks. Video deblurring poses a new set of challenges as compared to image deblurring due to dimensionality as well as information connected in frames. GAN has been quite successful in the image deblurring tasks, taking inspiration from this we have proposed deep salient video deblurring (DSVD) framework using GAN. Saliency features help reduce the area of focus in the frames while guiding the GAN through the training phase. We have used video deblurring and GoPRO datasets to test our model and compared it to the various techniques we have discussed. In terms of peak signal-to-noise ratio (PSNR), our approach performs better than others. It particularly does well on heavily noised video frames. The results have been optimistic.
Fields of Study & Subjects
Advanced Image Processing Techniques
Generative Adversarial Networks and Image Synthesis
Image and Video Quality Assessment
Publication details
- Journal / source
- International Journal of Computational Vision and Robotics
- Publisher
- Inderscience Publishers
- Volume
- 17
- Issue
- 2
- Pages
- 159–177
- Publication date
- 1 January 2026
- Language
- EN