Optimization and Application of Deblurring Algorithm for Photographic Images

Because of camera shake, out-of-focus or object motion, photographic images can cause image blur. Based on improving the clarity of photographic images, this study proposes an end-to-end image deblurring method based on CNN, which adopts feature pyramid network (FPN) as the infrastructure and enhances the adaptability of the network to objects of different sizes through multi-scale processing strategy. On the basis of FPN, the feature enhancement module is introduced, and the quality of image detail restoration is further improved by feature fusion between different levels of the network. In addition, in order to optimize the deblurring effect, the confrontation training mechanism of generator and discriminator is designed. The generator generates the deblurred image, and the discriminator is used to evaluate the difference between the deblurred image and the real clear image. Experimental results show that the proposed method improves the peak signal-to-noise ratio (PSNR) from 17 dB to 43dB (taking the first group of images as an example), and the structural similarity index (SSIM) is between 0.9-1, which shows excellent performance in image deblurring.