Video Image Deblurring Algorithm Based on Denoising Engine

Aiming at the problem that blurred digital video is easy to lose inter-frame information and ignore spatiotemporal during restoration, a video image deblurring algorithm based on denoising engine is proposed. We extend the adaptive Laplacian regularization term constructed by the denoising engine to the field of video image restoration. Firstly, the video image self-similarity redundant information can be gained through the nonlocal means (NLM) regularization, and then we present a new restoration model by mixing different regularizers, especially by combining NLM regularizer with the denoising regularizer. To solve the video image restoration model, we used the simplest gradient descent method. The experimental results show that our method has good deblurring effect and certain robustness to noise.