###### Abstract
The performance of these physical model-based methods is highly dependent on the precise estimation of multiple parameters. Consequently, even minor inaccuracies in these estimations can cause the model to produce inconsistent or degraded results, particularly when applied to images from diverse underwater environments [11]. Non-physical enhancement models, instead of estimating any parameters of the underwater environment, focus on image enhancement with statistical changes, contrast enhancement, color correction and image fusion steps. For example, Relative Global Histogram Stretching (RGHS) [12], which is based on histogram equalization, focuses on contrast enhancement and color compensation by expanding the histogram of the blue and green channels, which are the dominant colors underwater, according to the global distribution. Hue-Preserving (HP) [13] aims to preserve the original color tone by applying histogram stretching to HSI/HSV color spaces. Color Balance and Fusion (CBF) [14] divides the input image into two copies, increases the contrast of one, and applies color balancing with white balance correction to the other. The two images are fused by the Laplacian pyramid to generate the final image. However, it may produce red-colored artifacts in the image. The Hybrid Fusion Method (HFM) [15] aims to comprehensively correct underwater images by blending images with multiple enhancement strategies: color and white balancing, visibility enhancement, contrast enhancement, and perceptual image fusion. The method proposed by [11], combining the improved Retinex output with Adaptive color correction with NSST-based multi-scale fusion, preserves some advantages of the physical model while offering fusion flexibility [11]. In addition, Bayesian Retinex [16] minimizes color and brightness aberrations with a statistical decomposition; Modified Color Correction + Adaptive LUT [17] limits artifacts with edge-preserving filters and LUT-based contrast enhancement. Minimal Color Loss + Locally Adaptive Contrast [18] offers a balance that minimizes color losses while adaptively increasing local contrast. In short,although methods that are not based on physical models stand out due to their fast and easy applicability, side effects such as excess color saturation or contrast, extra noise or poor generalization ability can be observed with the use of more than one method. However, acceptable results can be achieved with appropriate method selection and fine tuning [11]. Deep-learning-based methods with strong problem-solving capabilities in the field of image processing produce high-quality results by learning the mapping between corrupted images and reference images; however, they require large datasets to strengthen their generalization capabilities. A Fast Underwater Image Enhancement Generative Adversarial Network (FUnIE-GAN) [19] is a conditional convolutional GAN model. It aims to eliminate chromatic aberrations and low contrast of corrupted underwater images in real time. However, since it is a supervised learning model, it creates a large dataset with synthetic underwater images. This leads to generalization problems of the model. To address degradation diversity, [PERSON] et al. [19] introduced SCNet to learn \"water type desensitized\" representations using novel normalization schemes; however, this supervised approach still relies on paired training data and its effectiveness can be limited in images suffering from extreme color casts or heavy backscatter. Although [PERSON] et al. [20] proposed Ucolor, a hybrid model using multi-color spaces, its visual results often exhibit significant color distortions and low contrast, leading to images that can appear unnatural and unrealistic. Target-Oriented Perceptual Adversarial Learning (TOPAL) [21] is a GAN-based model that focuses on improving object detection performance while enhancing underwater images. This requires multiple loss calculations, their simultaneous optimization, and specific training for each application, limiting the flexibility of the model.