Effective video deblurring based on feature-enhanced deep learning network for daytime and nighttime images

Abstract

Motion-blurred images are usually generated when captured with a handheld or wearable video camera, owing to rapid movement of the camera or foreground (i.e., moving object captured). Most traditional algorithm-based approaches cannot effectively restore the nonlinear motion-blurred images. Deep learning network-based approaches with intensive computations have recently been developed for deblurring blind motion-blurred images. However, they still achieve limited effect in restoring the details of the images, especially for blurred nighttime images. To effectively deblur the blurred daytime and nighttime images, the proposed video deblurring method consists of three major parts: an image storage module (storing the previous deblurred frame), adjacent frames alignment module (performing optimal feature point selection and perspective transformation matrix), and video-deblurring neural network module (containing two sub-networks of single image deblurring and adjacent frames fusion deblurring). The proposed approach’s main strategy is to design a blurred attention block to extract more effective features (especially for nighttime images) to restore the edges or details of objects. Additionally, the skip connection is introduced into such two sub-networks to improve the model’s ability to fuse contextual features across different layers to enhance the deblurring effect further. Quantitative evaluations demonstrate that our method achieves an average PSNR of 32.401 dB and SSIM of 0.9107, surpassing the next-best method by 1.635 dB in PSNR and 0.0381 in SSIM. Such improvements reveal the effectiveness of the proposed approach in addressing deblurring challenges across both daytime and nighttime scenarios, especially for making the alphanumeric characters in the really blurred nighttime images legible.