AVM Image Quality Enhancement by Synthetic Image Learning for Supervised Deblurring

Abstract

An Around View Monitoring (AVM) system is widely used to allow a driver to watch the situation around a car. The AVM image is generated by image distortion correction and viewpoint transformation for images captured by wide view-angle cameras installed on the car. However, the AVM image is blurred due to these transformations. This blur impairs the visibility of the driver. While many deblurring methods based on CNN have been proposed, these general-purpose de-blurring methods are not designed for the AVM image. (1) Since the blur level in the AVM image is region-dependent, deblurring for the AVM should also be region-dependent. (2) Furthermore, while supervised deblurring methods require a pair of input-blurred and output-deblurred images, it is not easy to collect the deblurred AVM image. This paper proposes a method for generating the pairs of training images that cope with the aforementioned two problems. These training images are generated by the inverse transformation of the AVM image generation process. Experimental results show that our method can suppress blur on AVM images. We also confirmed that even a very shallow CNN with the inference time of 2.1ms has the same performance as the SoTA model.