Water-Air Interface Imaging: Recovering the Images Distorted by Surface Waves via an Efficient Registration Algorithm

Abstract

Imaging through the wavy water–air interface is challenging since the random fluctuations of water will cause complex geometric distortion and motion blur in the images, seriously affecting the effective identification of the monitored object. Considering the problems of image recovery accuracy and computational efficiency, an efficient reconstruction scheme that combines lucky-patch search and image registration technologies was proposed in this paper. Firstly, a high-quality reference frame is rebuilt using a lucky-patch search strategy. Then an iterative registration algorithm is employed to remove severe geometric distortions by registering warped frames to the reference frame. During the registration process, we integrate JADE and LBFGS algorithms as an optimization strategy to expedite the control parameter optimization process. Finally, the registered frames are refined using PCA and the lucky-patch search algorithm to remove residual distortions and random noise. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of sharpness and contrast.

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

“…This includes removing or correcting deformations and distortions in the image and restoring the true shape and position of the object. Commonly used restoration methods include image alignment based on feature point matching [42,45], deformation modeling and correction [46], and filtering algorithms to remove water surface fluctuations. These methods are aimed at transforming and filtering the image according to the distortion features, making the restored image reflect the appearance of the original object more accurately.…”

“…For the cross-media distorted image restoration experiments, in order to evaluate the performance of the proposed algorithm in conventional scenarios, the algorithm of this paper is compared with the more advanced methods proposed by the existing AWTVFFNet [40], UnfairGAN [41], LPF [42], and CARNet [43]. The mean square error, peak signal-to-noise ratio, structural similarity, and time for image restoration of this paper's algorithm and the compared algorithms are shown in Figure 8.…”