dehazing

Dehazing is a process in image processing and computer vision aimed at removing atmospheric haze or fog from digital images. Haze significantly degrades image quality by reducing contrast, obscuring details, and altering colors. The primary goal of dehazing is to recover a clear and visually appealing image that resembles how the scene would appear without atmospheric interference.

Several methods exist for dehazing, broadly categorized into model-based and learning-based approaches. Model-based methods typically rely on atmospheric scattering models that describe how light is attenuated and scattered by particles in the atmosphere. These models often involve estimating parameters such as the transmission map, which represents the degree of light transmission through the atmosphere, and the atmospheric light, which is the light scattered from distant objects. By inverting these models, the haze-free image can be reconstructed. Common techniques include Dark Channel Prior, which assumes that some pixels in the haze-free image have very low intensity in at least one color channel, and color attenuation prior.

Learning-based methods, particularly those employing deep neural networks, have gained significant traction due to their ability to learn complex haze patterns directly from data. These methods often involve training convolutional neural networks (CNNs) on large datasets of hazy and corresponding clear images. The network learns to map hazy images to their clear counterparts, effectively learning the dehazing process without explicitly relying on physical atmospheric models. Deep learning approaches have demonstrated state-of-the-art performance in terms of both visual quality and objective metrics.

Dehazing finds applications in various fields, including autonomous driving, surveillance, remote sensing, and photography, where clear visibility is crucial for accurate perception and analysis.