# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 1 Introduction  
The integration of fog removal capabilities into fixed surveillance cameras is a critical advancement for enhancing safety, security, and public order [1]. In environments where visibility is frequently compromised by atmospheric conditions such as fog, the ability to maintain clear and reliable imagery becomes paramount. Surveillance systems, tasked with monitoring critical infrastructure, public spaces, and transportation networks, must operate effectively under adverse weather conditions to ensure continuous protection and situational awareness [2]. Fog, as a natural phenomenon, obscures visual details, reduces contrast, and hampers the detection of objects or individuals, posing significant risks to security operations. Consequently, equipping surveillance cameras with robust fog removal functionalities is not merely an enhancement but a necessity for modern safety systems [3, 4]. Extensive research has been conducted on fog removal networks, yielding remarkable progress in restoring visibility from degraded images [5, 6]. Techniques ranging from traditional approaches like the Dark Channel Prior (DCP) [7] to advanced deep learning methods such as convolutional neural networks (CNNs) and GridNet architectures [8] have demonstrated substantial improvements in dehazing performance. These studies have collectively contributed to a deeper understanding of image restoration under foggy conditions, achieving impressive results in various scenarios [9, 10, 11]. However, a significant challenge persists in the domain of fog removal research: the acquisition of paired datasets--images of the same scene captured both with and without fog. For effectivetraining and evaluation of dehazing algorithms, datasets must include both indoor and outdoor scenes with synthetically or naturally induced fog [12]. In practice, obtaining such paired images is exceedingly difficult due to the variability of environmental conditions and the impracticality of controlling natural fog in real-world settings. Photographing identical scenes under foggy and clear conditions simultaneously is a logistical challenge, often rendering traditional data collection methods insufficient. To address this limitation, the generation of synthetic fog becomes a pivotal step in advancing fog removal research [13]. While numerous studies have focused on dehazing [14; 15], fewer have explored the equally critical task of fog synthesis [16]. The ability to artificially create fog in a controlled manner enables researchers to simulate diverse foggy conditions, thereby facilitating the development and validation of dehazing algorithms. Existing fog generation techniques include simplistic approaches, such as uniform fog application [17], as well as more sophisticated methods like dark channel-based synthesis [7] and distance-aware (depth-based) fog generation [18]. Although these methods provide a viable means to simulate fog, they often lack the realism and adaptability required to mirror natural fog distributions accurately. Moreover, synthetic fog generated through rudimentary techniques may not adequately challenge the robustness of modern dehazing networks, potentially leading to overfitting or suboptimal performance when applied to real-world foggy scenes. Among the various approaches to fog synthesis, the development of a dedicated fog generation network emerges as a promising solution [16]. Unlike traditional methods that rely on heuristic assumptions or physical models, a neural network-based fog generator can learn complex patterns of fog distribution directly from data, producing more realistic and contextually relevant foggy images. A key insight driving this research is that the efficacy of a fog generation network is closely tied to the architecture of the dehazing network it aims to complement [8]. Specifically, leveraging a high-performing dehazing network as the backbone of the fog generation process ensures that the synthesized fog aligns with the characteristics that advanced dehazing algorithms are designed to address.