# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 1 Introduction  
Haze image dehazing, a critical task in computer vision and image processing, has become increasingly important in various applications such as autonomous driving, surveillance systems, and remote sensing [1; 2]. Haze, a common atmospheric phenomenon, significantly reduces visibility in outdoor scenes, degrading image quality and decreasing the accuracy of object recognition and classification tasks [3; 4]. This degradation poses serious challenges in many computer vision applications, necessitating the development of effective haze removal techniques [5; 6]. The impact of haze on visual perception extends beyond mere aesthetic concerns, as it can severely compromise the functionality of critical systems that rely on clear visual input, potentially leading to safety issues in autonomous vehicles or misidentifications in security systems. The history of haze removal techniques dates back to early methods based on physical exposure adjustments. These approaches attempted to remove haze by combining multiple images taken with different exposure times [7]. While somewhat effective for static scenes, these methods faced significant challenges when applied to dynamic scenes or in situations requiring real-time processing [8; 9]. The need for specialized camera equipment and complex post-processing steps limited their practical applicability in many real-world scenarios [10; 11]. These early attempts, although limited in their scope, laid the groundwork for understanding the complex nature of haze and its interaction with light, paving the way for more sophisticated techniques. To overcome the limitations of physical exposure-based methods, researchers developed image processing algorithms for haze removal. These methods aimed to enhanceimage contrast or reduce the haze effect through color correction techniques such as histogram equalization and unsharp masking [12]. However, these approaches often struggled with complex scenes or varying weather conditions, failing to produce satisfactory results when haze density was non-uniform or when dealing with scenes containing intricate depth information [13; 14]. The shortcomings of these methods highlighted the need for more progressive and context-aware approaches to haze removal. A significant breakthrough in the field came with the introduction of the Dark Channel Prior (DCP) method by [PERSON] et al. [15]. This method leverages the observation that in most haze-free outdoor images, at least one color channel in the RGB(Red, Green, Blue) space has very low intensity in some local regions. By utilizing this prior knowledge to estimate haze transmission, the DCP method effectively removes haze from single images. Despite its groundbreaking nature, the DCP method faced challenges in real-time applications due to its high computational complexity, and it tended to overcorrect in certain scenes, such as those with bright skies [7; 16]. The DCP method's success sparked a new wave of research into prior-based dehazing techniques, inspiring numerous variations and improvements. Building upon the insights gained from the DCP method, subsequent researchers explored haze removal techniques that incorporated depth information of the scene [17; 18]. These approaches are based on the principle that haze density typically increases with distance from the camera. Various technologies, including stereo vision, structured light, and depth sensors, have been employed to estimate depth information [19]. While these methods can theoretically achieve superior performance by accurately modeling haze distribution, obtaining precise depth information in real-world applications remains a significant challenge, particularly for distant objects or scenes with complex geometric structures [20; 21]. The integration of depth information opened up new possibilities for more accurate haze removal, especially in scenarios where the haze distribution is highly non-uniform. The advent of deep learning has ushered in a new era in image dehazing research.