###### Abstract  
The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (\(p<0.01\)). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. underwater image enhancement; unsupervised deep learning; image fusion; detail enhancement; Latent Low-Rank Representation 20252025578830390  
such as color loss, low contrast, hazy and blurry appearance in underwater images [5]. This situation distorts the real colors and appearance of objects and the environment, culminating in perceptual weaknesses (bluish and greenish appearance), and it is a key limitation for many research areas. Therefore, in image-based underwater applications, images are first preprocessed with an image enhancement algorithm. However, owing to the complexity of the underwater environment, many advanced methods apply multiple sequential or co-occurring enhancement steps (color correction, contrast enhancement, noise reduction, haze removal and detail enhancement) and image fusion strategies to eliminate these distortions [5]. According to existing research, underwater image enhancement methods are divided into two main categories: physical models and non-physical models. In addition, deep learning models have recently been studied as a subfield of non-physical models. Physical models are based on the principle of reversing the effects of light absorption and scattering in underwater images. They focus on compensating for color loss in images and removing hazy images by estimating how much each pixel is attenuated underwater and the scattered light. For example, Image Blurriness and Light Absorption (IBLA) [6] estimates the transmission map of the light lost due to background light and absorption and scattering; Wavelength Compensation and Dehazing (WCD) [7] focuses on estimating the depth of the scene using the relationship between image blur and light absorption. Underwater Light Attenuation Prior (ULAP) [8] corrects the underwater image according to the physical model by performing fast depth estimation. Underwater Dark Channel Prior (UDCP) [9] and Underwater Total Variation (UTV) [10] adapt the dark channel assumption to the underwater environment and adapt the background light. To summarize, physically based methods are based on the high accuracy estimation of many parameters of the underwater environment such as absorption/scattering, depth map and background light.