## 4 Conclusions  
The method proposed in this study offers a hybrid underwater image enhancement approach focused on a single scene, which does not require a large amount of dataset and reference images. The proposed detail enhancement unit, as one of the basic building blocks of the method, combines multi-scale detail highlighting with structural decomposition, enabling both the highlighting of real details and the suppression of noise and artifacts. This unit produces a simplified content that preserves structural information and directly contributes to the final fusion quality. In addition, the method, which combines image-processing techniques with the powerful representation capacity of unsupervised deep learning, minimizes fundamental distortions such as chromatic aberration, contrast loss and structural blurring caused by the intense absorption and scattering of light in the underwater environment. In order to demonstrate the effectiveness of the method, experiments conducted on images obtained from different underwater environments such as hazy, bluish, greenish and yellowish quantitatively demonstrate that the proposed method outperforms the current state-of-the-art methods in metrics such as UIQM, UICM, IL-NIQE, IE and AG. In addition, in the qualitative evaluation, it is observed that it visually eliminates color distortions, clarifies the details and produces results more suitable for the human visual system. The proposed method also achieves successful results in tests performed with Scale-Invariant Feature Transform (SIFT) and produces results comparable to those of outstanding approaches such as [PERSON] et al. 's and HFM. The statistical significance of the proposed method's performance was rigorously evaluated using the Wilcoxon signed-rank test on all reference-free quality metrics. This analysis substantiates that the observed superiority is not a random occurrence, thereby statistically validating the effectiveness of our approach. As a result, the proposed hybrid method makes significant progress in the field of underwater image enhancement and contributes to obtaining higher quality and detailed underwater images in various applications. While the proposed framework has demonstrated strong performance in enhancing underwater images, it is important to acknowledge its inherent limitations, which also pave the way for future research. The primary limitation is the trade-off between detail enhance  
Figure 8: Quantitative comparison of enhanced images in terms of IL-NIQE (\(\downarrow\)), AG (\(\uparrow\)), IE (\(\uparrow\)), and UIQM (\(\uparrow\)) for three different rows presented in Figure 2. The proposed method consistently achieves lower IL-NIQE scores and higher AG and UIQM scores, indicating superior perceptual quality and edge sharpness across all cases [11]. ment and potential noise amplification. Our method's design prioritizes the recovery of fine textures and structural details; a consequence of this is that in some images, latent noise can become more prominent compared to methods that employ heavier smoothing. A second practical limitation is the computational cost. As the primary focus of this study was to validate the model's effectiveness, the current implementation has not been optimized for runtime speed, which may limit its use in real-time applications. Future work will focus on two key areas: (1) integrating more sophisticated noise-aware modules that can better distinguish between fine textures and unwanted noise, and (2) optimizing the framework for computational efficiency to facilitate its deployment on resource-constrained platforms. This approach can both reduce processing time and facilitate the integration of the method into embedded systems and real-time underwater imaging applications. Conceptualization, S.K. and E.A. ; Methodology, S.K. and E.A. ; Software, S.K. ; Validation, S.K. ; Formal Analysis, S.K. ; Investigation, S.K. ; Resources, S.K. ; Data Curation, S.K. ; Writing--Original Draft Preparation, S.K. ; Writing--Review and Editing, S.K. and E.A.