Underwater Image Enhancement with JetsonXavier

Abstract

In recent years, unmanned underwater vehicle(UUV) has attracted many attention for ocean exploration. For a UUV, the object recognition makes an important role for automatic cruising. However, underwater object recognition meets some special difficulties due to the non-linear image deterioration. Furthermore, in the case of a neural network(NN) based object recognition system, real-time processing is required embedded GPU. In this paper, we evaluate the real-time performance of the existing technique on the GPU module (JetsonXavier NX) for the embodiment, and a new correction method is proposed based on the evaluation results. In addition, the structural similarity and the real time performance of the existing algorithms are implemented on JetsonXavirer NX to make a comparison. Finally a new neural network is proposed to enable real-time processing of recognition based on the evaluation result. The proposed method improved peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) in the region having fewer red light, compared to previous methods. Furthermore, this method can restore the perceptual and statistical qualities of the distorted images in real-time.