# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 6 Conclusions  
In this study, we introduce a denoising multiscale back-projection feature fusion network designed to address multiscale feature fusion and complex degradation. Our core module, DeMBFF, introduces the back-projection algorithm and denoising block into multiscale feature fusion for underwater image enhancement. By leveraging multiscale back-projection feature fusion, the network iteratively feeds errors back for image enhancement. The combination of denoising blocks enables DeMBFF to enhance deblurring performance. The multiple degradation knowledge distillation strategy adapts the model to accommodate multiple degradation scenarios. Through qualitative and quantitative experiments on standard datasets, the results demonstrate that DeMBFF-Net performs well across various datasets and significantly improves image color and clarity. In particular, it excels in addressing underwater images with complex degradation, which achieves a balance between color correction and deblurring for underwater images. In the future, we will seek to improve the model's performance for images with low illumination and enhance color consistency and brightness for heavily degraded images. Conceptualization, W.Q. ; Methodology, W.Q. and Y.S. ; Validation, J.C.; Data curation, Y.S. ; Writing--original draft, Y.S. ; Writing--review & editing, W.Q. ; Visualization, J.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This data can be  
This research received no external funding. This data can be  
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