# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 1 Introduction  
In recent years, the processing of underwater images has attracted increasing attention as marine activities increase. Underwater vision plays a crucial role in various applications, including marine biology, underwater archeology, and underwater surveillance. However, underwater vision is still challenging for several reasons. The fog effect produced by suspended particles during the absorption and scattering of light leads to blurred underwater imaging. The difference in the attenuation rate of the light of different wavelengths causes underwater images to show blue shifts or green shifts. The light absorption and scattering in water results in real-world underwater images becoming seriously degraded, causing issues such as color distortion, low contrast, and blurring. The degradation of underwater images severely influences subsequent image-processing tasks, such as automatic object detection and recognition. As underwater images are similar to low-quality images in the air, early works [1] directly applied traditional image-processing techniques (histogram equalization and gray world) to solve this problem. They usually focus on underwater images with certain kinds of degradation. In addition, these methods easily fail for images with complex degradation. In order to determine the reason for this degradation, many studies [2, 3] have analyzed the underwater imaging process and constructed physical models. Therefore,the problem is transformed into an estimation of the parameters of the physical models. However, the accuracy of parameter estimation will largely influence the final performance. In addition, the model cannot adapt to different environments. The traditional methods strongly depend on prior knowledge, which leads to poor results in scenarios where the prior assumptions are not valid. Recently, deep learning-based underwater image enhancement has become prevalent because of its robustness to different types of underwater images [4; 5; 6; 7]. Compared with traditional underwater image enhancement methods, deep learning-based underwater image enhancement methods do not rely on prior knowledge, have fewer limitations, and achieve better results in most underwater image enhancement. However, several issues remain unsolved. First, most deep learning-based underwater image enhancement methods acquire features at multiple scales [8; 9; 10; 11], and effective fusion mechanisms for multiscale features are lacking. A large amount of detailed information is lost through the encoder-decoder structure due to the downsampling and upsampling processes. On the other hand, many existing methods fail to restore details for underwater images due to noise in the images, especially for complex degradation, such as the combination of color casting and blurring. In order to restore local details in underwater images, an ideal underwater image enhancement method should achieve a balance of color, contrast, and sharpness. Furthermore, the extension of underwater image enhancement methods to handle multiple degradation types remains relatively unexplored. Given the similarity in degradation patterns across various types of images, adapting models to accommodate multiple degradation scenarios is crucial for broader applicability. In order to address the aforementioned problems and limitations, we propose a denoising multiscale back-projection feature fusion network (DeMBFF-Net) for real-world underwater image enhancement. A multiscale back-projection feature fusion mechanism is introduced for the encoders and decoders. The network can capture both the global context and fine details by processing the feature maps at different scales simultaneously. The back-projection processes iteratively feed the error back during feature fusion, which improves the quality of the recovered images. In order to improve the restoration performance for images with noise, we designed two kinds of denoising blocks for the encoders and decoders.