# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 1 Introduction  
Photographs taken in natural settings frequently encounter adverse conditions. Weather fluctuations, atmospheric contaminants, haze, fire, smoke, and dust can degrade image quality. Hazy images suffer from reduced visibility due to fog, dust scattering, and light attenuation, complicating object identification and color fidelity. Addressing the challenges posed by hazy images constitutes a significant area of research in image processing, with various approaches being explored [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. Techniques for haze removal, such as Markov random field (MRF) [1], filtering [3; 5], color analysis [2; 7; 9; 10], transmission map estimation [4; 6; 8], and deep learning approaches [11; 12], play pivotal roles in enhancing visibility compromised by haze in the resulting images.  
[PERSON] et al. [4] presented a seminal work on transmission map estimation using the dark channel prior (DCP) notion. This method capitalizes on the observation that, within local regions of outdoor images, at least one color channel typically exhibits markedly low intensity, which they term the _dark channel_. They noted that the dark channel tends to become brighter in the presence of haze. The algorithm effectively estimates and mitigates fog, leveraging this characteristic alteration and enhancing visibility and image clarity in outdoor settings.  
However, in situations where the color of the target object closely matches atmospheric light and lacks shadows, the DCP exhibits constraints, potentially misidentifying such instances as haze. Additionally, during haze depth estimation, the transmission map may incorrectly interpret sky regions as heavily fogged, leading to inaccuracies. The restoration process could introduce image distortions in areas with significant fog due to over-correction. Moreover, utilizing a patch size of \(15\times 15\) for DCP computation might leave halo artifacts.  
Various efforts have been made to enhance the DCP-based transmission map estimation approach [6; 8; 13]. These refinements are geared towards enhancing the precision oftransmission map estimation. While techniques such as boundary constraints [6], multiscale Laplacian and Gaussian pyramids [8], and dual transmission map estimation [13] show promise, challenges persist in addressing night-time haze and mitigating halo artifacts.  
Drawing from the insights of [4], we extend the discourse with fresh perspectives and innovations by proposing an approach for image dehazing. We introduce a method for estimating enhanced transmission maps utilizing multiple \(1\times 1\) DCPs derived from multiscale hazy, inverted, and Euclidean difference images which strive to enhance the granularity of transmission maps. To tackle the challenge posed by low-light, hazy images, we propose a new technique for airlight estimation. Furthermore, we introduce an adaptive gamma correction method to refine the transmission map further.  
Our proposed method outperforms the next-best technique by achieving a significantly higher score of approximately 3.03 points on the Dehazing Quality Index (DHQI) [14] within a Real-World Task-Driven Testing Set (RTTS) [15].