# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 2 Related Work  
### Underwater Image Enhancement  
Our work is related to single underwater image enhancement. According to the information used, single underwater image enhancement can be divided into three categories: nonmodal-based, modal-based, and data-driven methods. **Nonmodel-based methods:** Nonmodel-based methods, such as histogram stretching, histogram equalization, and white balance, directly adjust image pixel values rather than establishing mathematical or physical models. Histogram equalization and its variants are commonly used to enhance the contrast of images. [PERSON] et al. [1] improved the contrast and saturation of underwater images by stretching the pixel range in the RGB color space and HSV color space. In addition to low contrast, underwater images also have significant color deviation. As classical image color correction algorithms, the white-balance and gray world methods are widely applied to underwater image enhancement. [PERSON] et al. [12] proposed a fusion method that blends a contrast-enhanced image and a color-corrected image at multiple scales. [PERSON] et al. [13] combined pixel distribution remapping with a multipriority Retinex variational model to address color shift and brightness loss, which efficiently improves image clarity. MCLA [14] utilizes multicolor model conversion and leverages background light and transmission maps, which achieves a good balance between brightness and visibility. [PERSON] et al. [15] address the nonuniform feature drift problem by using multi-interval subhistogram perspective equalization. This kind of method improves the contrast, brightness, and color of the image but tends to produce undesired artifacts, color cast, or overexposure in the results. **Physical model-based methods:** Physical models are usually designed for underwater image restoration to simulate the degradation process of underwater images. By estimating the parameters of the model, the degradation process can be inverted to obtain the normal image. Due to the similarity between underwater imaging models and atmospheric scattering models, many image dehazing algorithms have been applied to underwater image restoration. One of the most influential techniques was dark channel prior (DCP), which was proposed by [PERSON] et al. [16]. Based on this, [PERSON] et al. [17] enhanced underwater images by using a statistical model. [PERSON] et al. [18] proposed a novel imaging formation model based on a restoration strategy combined with prior knowledge and unsupervised techniques. Since model-based methods assume that underwater images follow a specific physical model, these methods fail when the assumption is not true in the real world. **Data-driven method:** With the success of deep learning in many computer vision tasks, it is natural for many researchers to apply these methods to underwater image processing. Early attempts applied deep neural networks to estimate the parameters of physical models, such as background illumination and transmission maps. Therefore, they can be seen as hybrids of physical methods and data-driven methods. The other line of research is to use an end-to-end neural network to achieve enhanced underwater images directly. Deep learning-based underwater image enhancement methods can be classified according to the network structure used, such as CNN-based and GAN-based approaches. Convolutional neural network-based methods can effectively learn the features of underwater images and remove blurring and color bias from them. In order to better remove blur and color bias from underwater images, UIE-Net [19], which consists of three subnetworks that are responsible for feature extraction, color correction, and defogging, has been used. The gated aggregated convolutional neural network WaterNet [20] feeds the enhanced image obtained via white balance, gamma correction, and histogram equalization to the neural network, together with the original image.