Real-time image acquisition and deblurring for underwater gravel extraction by smartphone

Abstract

Gravel size distribution is an important aspect of stream investigation. Using water photography to determine such distribution is a simple and cost-effective approach for gathering instream gravel information. However, good-quality images of underwater gravels in shallow areas are difficult to acquire because of the flow- and wind-induced perturbation at water surface. Thus, two Lucy–Richardson iterations are applied on an averaged image to obtain a deblurred image for gravel extraction. A Matlab code for multi-frame image averaging and image deblurring is implemented on a laptop computer. Underwater gravel images are acquired using a video camera and processed offline. Thus, the usability of the images acquired during field investigation cannot be determined immediately. However, returning to the investigated streams for additional data gathering would be costly, and the cameras may accidentally be dropped into the water. This paper presents multi-frame image averaging and image deblurring smartphone-based approaches for underwater gravel extraction. A waterproof smartphone is used to acquire the images, on which image deblurring is immediately conducted to test whether the images can be used for gravel extraction. The averaged image of using mean-based filter is derived during real-time image acquisition. The deblurred image is derived block-by-block because of limited memory capacity of smartphones. The time consumed for acquiring 1500 frame images with size of 1280 × 720 pixels is approximately 6 min by Sony Xperia smartphones. Image averaging can be performed in real time during image acquisition. Image deblurring is accomplished accurately and is consistent with results of the Matlab code. The processing time for image deblurring is approximately 12 min. A compact system for underwater gravel investigation using smartphones is successfully developed in this study. Image acquisition and deblurring are completed in real time at the investigated fields. Thus, we can immediately test whether the acquired images are usable for gravel extraction, thereby improving investigation efficiency significantly.