LIDN: A novel light invariant image dehazing network

Abstract

With the increasing demand for efficient image dehazing solutions in computer vision algorithms, particularly for autonomous systems, a research focus on low-inference time yet high-performance dehazing techniques has emerged. Existing dehazing methods predominantly rely on either daytime or nighttime haze models, limiting their effectiveness in handling haziness under varying lighting conditions. To address this limitation, this research paper introduces the Light Invariant Dehazing Network (LIDN), an end-to-end image dehazing network consisting of four sub-modules: Feature Extractor, Deep Global Atmospheric Light Estimator, Medium Transmission Extractor, and Encoder-Decoder. The proposed model, trained using Quadruplet loss, effectively reduces artifacts and produces sharper dehazed images. Extensive experiments conducted under diverse lighting conditions demonstrate the superior performance of the proposed LIDN model compared to state-of-the-art daytime and nighttime dehazing approaches. Remarkably, the proposed model achieves these exceptional results with a runtime of only 0.24s per image, making it highly efficient than existing dehazing algorithms.