## 1 Introduction  
Low-light imaging remains a fundamental challenge in computer vision, as adverse lighting conditions degrade the visual quality of captured images, introducing severe noise, poor contrast, color distortions, and the loss of structural detail. These degradations significantly impact both the perceptual quality for human observers and the performance of downstream automated vision systems. Applications such as surveillance, autonomous driving, mobile photography, and medical diagnostics frequently suffer from poor performance due to visibility issues under low illumination [1, 2]. This challenge is further compounded in underwater environments, where severe light absorption and scattering disturb both color and contrast. As a result, a growing body of work has explored underwater low-light image enhancement using discriminative learning [3], reinforcement learning guided by perceptual cues [4], and large foundation models [5]. Traditional image enhancement techniques, including histogram equalization, gamma correction, and Retinex-based methods, are widely used due to their computational efficiency and simplicity. However, these algorithms often fail to generalize across diverse lighting scenarios and tend to produce over-enhanced or artifact-laden outputs when applied to complex real-world scenes [6, 7]. These limitations have prompted the research community to adopt data-driven solutions based on deep learning, particularly convolutional neural networks (CNNs), to learn robust mappings from low-light to well-lit image domains [8; 9; 10]. Among these, several supervised learning approaches have achieved remarkable visual quality by leveraging large-scale datasets of paired low-/normal-light images [11; 12]. However, collecting and aligning such pairs is labor-intensive and impractical in many real-world contexts. To address this, recent advances have proposed unsupervised, self-supervised, and zero-reference frameworks, such as Zero-DCE [9] and RetinexDIP [10], which alleviate the need for labeled data while still achieving compelling results. Despite this progress, most high-performing models are computationally demanding and not suitable for deployment in resource-constrained environments like mobile phones, embedded systems, or real-time platforms. Recent works have attempted to bridge this gap by developing lightweight architectures [13; 14; 15] or designing cascaded and progressively refined pipelines [16; 17], but a clear trade-off between image quality and model efficiency remains. In this work, we propose a lightweight (i.e., low-parameter and low-FLOP) deep learning architecture for low-light image enhancement that is suitable for real-time inference on edge devices. Our model builds upon a UNet-inspired encoder-decoder backbone and integrates attention modules to dynamically suppress noise and highlight meaningful features. The network is trained using a composite loss function combining pixel-wise reconstruction, structural similarity (SSIM), and perceptual quality metrics. To further support data efficiency and generalization, we construct a hybrid dataset comprising both real low-light images from established benchmarks and synthetic image pairs generated through physically inspired exposure manipulation and noise modeling applied to clean images. This enables more flexible training and better simulation of varied lighting conditions. The main contributions of this paper are as follows:  
* We propose a lightweight, attention-augmented deep neural network architecture tailored for real-time low-light image enhancement. * We introduce a flexible pipeline for synthetic low-light data generation based on exposure manipulation and noise modeling. * We demonstrate the effectiveness of our model through extensive experiments on benchmark datasets (LOL, SID), achieving competitive performance in PSNR and SSIM while significantly reducing computational cost. * We highlight potential applications in industrial scenarios such as mobile photography, autonomous driving, and smart surveillance.