# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
###### Abstract
Although there are some studies on fringe noise removal, most of them use infrared images. For example, [PERSON] et al. [17] used the difference between the fringe noise component and the actual information component to solve a model using the alternating direction multiplier method in order to remove the fringe noise in infrared images. [PERSON] et al. [18] proposed a multi-scale image decomposition method based on improved LatLRR to smooth the fringe noise in infrared images by using the global low-rank structure feature of the fringe noise. However, as there are inconsistencies between the fringe noise characteristics of side-scan sonar and infrared images, these methods are not applicable to the former. The latest and most advanced stripe noise denoising methods are used for denoising infrared images; however, the imaging mechanisms of side-scan sonar images differ from those of infrared images, and their stripe noise characteristics are also different. We found through experimentation that these methods are not applicable, which we also pointed out in the introduction. In the example (using reference [17] as an example, as shown in Figure 1), the black stripe noise still exists in the denoised image. While specific challenges exist in removing stripe noise from side-scan sonar images, in the field of image processing, treating regions affected by stripe noise as anomalous regions in these images and using image restoration algorithms based on texture characteristics can effectively address this issue. Current image restoration algorithms can be broadly categorized into two types. The first type uses partial differential equations (PDEs) for restoration, initially proposed by [PERSON] et al. in 2000 [19]. This approach uses information from areas surrounding the damaged region, propagating known information into the area to be repaired in a coarse-to-fine manner, achieving good restoration results. Figure 1: The **left** side is the noisy image, and the **right** side is the denoised image. Subsequently, more complex PDE models were proposed [20; 21; 22]. However, PDE-based image restoration techniques excel in handling minor scratches and small-scale damage in images; however, when the damaged area and its surroundings are highly textured, the resulting restoration may be blurred. The second type uses sample-based texture synthesis for restoration. [PERSON] et al. proposed a priority-based texture synthesis algorithm [23], which operates on texture patches rather than individual pixels during the restoration process [24], thus reducing computational complexity. This algorithm has achieved satisfactory results in repairing large damaged areas; however, it has shortcomings in priority calculations and criteria for selecting matching blocks, often influenced by the structural texture characteristics of the damaged areas, resulting in lower accuracy. As a result, numerous researchers have investigated and proposed related improvements to the Criminisi algorithm [25; 26; 27; 28; 29]. Currently, in the field of image processing, machine learning and deep learning are widely applied. However, [PERSON] et al. [30] compared several methods, including Criminisi and deep learning, for denoising strip noise from side-scan sonar images and found that the Criminisi algorithm, as an early method, still demonstrates significant advantages. However, experimental findings indicate that applying the Criminisi algorithm to repair stripe noise in side-scan sonar images yields promising results, yet several issues remain, such as zero priority, texture blending, and computational speed. To address these issues, we propose improvements to the priority function, matching criteria, data terms, and confidence calculation, enhancing the algorithm's accuracy in restoration and achieving more satisfactory visual results.