Accelerated Blind Deblurring Method Via Video-Based Estimation in Next Point Spread Functions for Surveillance

Abstract

Blind deblurring has been attracting increased attention. In real-life problems, high-resolution images are needed to process and the blurring function, point spread function (PSF), is mostly unknown, especially in the surveillance systems such as camera integrated payload drop with a parachute. The PSFs are dependent on their previous functions, so we perform the deblurring process faster with our proposed model by integrating a previously prepared deep learning method. Our system consists of four phases: (i) enhancing images with an existing deep learning method, (ii) obtaining PSFs, (iii) predicting the next PSFs with our model, and (iv) enhancing the images with the wiener filtering we developed. The number of PSFs to be estimated was experimentally found as the point at which the PSNR value began to decrease in the test images. Convolutional LSTM layers were used for our model which has been compared with other state-of-the-art models in terms of performance and running time.