Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality such as color distortion, reduced contrast, and noise. Existing underwater image enhancement (UIE) methods often lack generalization capabilities, making them unable to adapt to various underwater images captured in different aquatic environments and lighting conditions. To address these challenges, a UIE method based on conditional denoising diffusion probabilistic model (DDPM) is proposed (DiffWater), which leverages the advantages of DDPM, and trains a stable and well-converged model capable of generating high-quality and diverse samples. Considering the multiple distortion issues in underwater imaging, unconditional DDPM may not achieve satisfactory enhancement and restoration results. Therefore, DiffWater utilizes the degraded underwater image with added color compensation as a conditional guide, through which the DiffWater achieves highquality restoration of degraded underwater images. Particularly, the proposed DiffWater introduces a color compensation method that performs channel-wise color compensation in the RGB color space, tailored to different water conditions and lighting scenarios, and utilizes this condition to guide the denoising process. In the experimental section, the proposed DiffWater method is tested on four real underwater image datasets and compared against existing methods. Experimental results demonstrate that DiffWater outperforms existing comparison methods in terms of enhancement quality and effectiveness, exhibiting stronger generalization capabilities and robustness

To use monostatic based imaging algorithms for multireceiver synthetic aperture sonar, the monostatic conversion is often carried out based on phase centre approximation, which is widely exploited by multireceiver SAS systems. This article presents a novel aspect for dealing with the multireceiver SAS imagery, which still depends on the idea of monostatic conversion. The approach in this article is based on Loffeld's bistatic formula that consists of two important terms, i.e., quasi monostatic and bistatic deformation terms. Our basic idea is to preprocess the bistatic deformation term and then incorporate the quasi monostatic term into an analogous monostatic spectrum. With this new spectrum, traditional imaging algorithms designed for monostatic synthetic aperture sonar can be easily exploited. In this article, we show that Loffeld's bistatic formula can be reduced to the same formula as spectrum based on phase centre approximation when certain conditions are met. Based on our error analysis, the maximum error magnitude of PCA method is about 1 rad, which would noticeably affect the SAS imagery. Fortunately, the error magnitude of presented method can be always kept within ${{\rm{\pi }} \mathord{/ {\vphantom {{\rm{\pi }} 4}} \kern-\nulldelimiterspace} 4}$ . It means that Loffeld's bistatic formula provides a more accurate approximation of the spectrum compared to that based on phase centre approximation. After that, this article develops a new imaging scheme and presents imaging results. Based on quantitative comparisons, the presented method well focuses multireceiver SAS data, and it provides better image compared to phase centre approximation method.

The phase center approximation (PCA) and the motion of the receiver array during reception are two main conditions that must be solved to use fast-Fourier-based imaging techniques in multireceiver synthetic aperture sonar (SAS). The former issue affects focusing results at close range while the latter one influences imaging performances in far distance. The processing result is that the uncompensated phase errors, introduced by those two issues, would degrade SAS imaging quality seriously. Through the analysis of outgoing and incoming acoustic paths, this paper derives an improved PCA method, considering the influence under those two approximations, describes the residual effects of narrow-beam approximations for PCA and “stop-and-hop”(S&H) errors in 2-D space domain, and presents imagery processed with chirp scaling (CS) algorithm. Based on the simulation and lake-trial results, the new processing method provides image quality better than conventional algorithms.

The statistical characteristic of ocean ambient noise plays an important role in developing underwater signal processors. Considering that the noise in shallow water shows the impulsive nature, non‐Gaussian noise models are usually applied to model the ocean ambient noise. In this study, the ocean ambient noise is modelled by using the Middleton Class B model, which can be decomposed into Gaussian and non‐Gaussian models. Then, the parameters of Class B model are estimated based on the least‐square estimation method, which can be deduced by using the characteristic function of the Middleton Class B model. The processing results of simulated data and real data indicate that the presented method well estimates the parameters of Class B modelled noise. Besides, it further shows that the Middleton Class B model is suitable for modelling the impulsive noise in shallow water.

With the Loffeld's bistatic formula, the point target reference spectrum (PTRS) of multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi monostatic term and bistatic deformation (BD) term. The former term is analogous to the monostatic SAS PTRS while the latter one describes the bistatic phase introduced by the bistatic displacement between the transmitter and receiver. The multireceiver SAS echo signal can be transformed into monostatic SAS equivalent data after correcting the BD term. Based on the range sub‐block processing method, this data transformation is conducted by using the preprocessing step. Then, fast imaging processors based on monostatic SAS system can be directly exploited to process the monostatic SAS equivalent data. In this paper, the authors mainly focus on the application of non‐linear chirp scaling algorithm. At last, the simulations and real data are used to validate the presented method. The processing results show that the imaging performance of presented method is nearly close to that of traditional method. Besides, the imaging efficiency is highly improved compared to the phase centre approximation (PCA) method.