# An Improved SIFT Underwater Image Stitching Method
## 3 Materials and Methods
### Underwater Image Pre-Processing  
When light traverses through water, a portion of it is reflected, while the remainder penetrates the water and is selectively absorbed. Different wavelengths of light experience varying degrees of absorption in water, with red light being significantly attenuated compared to blue-green light. Consequently, underwater images predominantly exhibit blue-green hues and tend to grow darker with increasing depth. Moreover, underwater images are susceptible to blurring and haziness due to the scattering of light by water, which can impair the accuracy of feature point extraction during the stitching process. Therefore, preprocessing of underwater images plays a crucial role, enabling the extraction of a larger number of feature points with enhanced precision.  
Underwater image preprocessing can be categorized into two main types: image enhancement methods, and image restoration techniques. While image restoration methods necessitate prior knowledge of the environmental conditions, image enhancement methods offer a more straightforward approach that does not rely on such specific information. As such, this paper employs image enhancement methods. Specifically, dynamic threshold white balance and CLAHE are utilized for the preprocessing of underwater images. The CLAHE method [28] enhances local contrast in images by applying a threshold to limit contrast amplification within localized regions, addressing issues such as noise overamplification that may occur in the adaptive histogram equalization (AHE) method [29]. The dynamic threshold white balance method [30], rooted in the YCbCr color space, offers image enhancement capabilities that enable adjustments to image tones and the restoration of accurate color representation. This ensures faithful rendering of the true colors of the captured subject in the images obtained by the camera.  
In this paper, we improve the quality of underwater images by optimizing their color and contrast. The dynamic threshold white balance method is used to adjust the color,while the CLAHE algorithm enhances the contrast and brightness. We set the contrast limit parameter of CLAHE to 2 and use a 4 \(\times\) 4 grid size to block the image.  
We selected a set of captured images as the preprocessed dataset. The results are shown in Figure 2. As can be seen, there was a significant improvement in image quality. Moreover, we used different methods to preprocess the images and perform image feature point matching. We compared our method with the integrated color model (ICM) [31], underwater light attenuation prior (ULAP) [32], underwater dark channel prior (UDCP) [33], and image blurriness and light absorption (IBLA) [34]. The results of the underwater image quality assessment are summarized in Table 1. The accuracy rate of interest point matching is improved in the preprocessed images, with more features correctly matched. Specifically, our method improves the accuracy rate from 44.76% in the original images to 59.06% in the preprocessed images. Compared with other algorithms, our algorithm has higher accuracy in feature matching. Overall, our proposed method effectively enhances the detection of interest points in underwater images.