Dark channel prior in low frequency domain for time-efficient single image dehazing

Dehazing plays a vital role in enhancing the quality of outdoor images for computer-based applications. While dehazing algorithms significantly improve outdoor image quality for computer vision tasks, their effectiveness hinges on two crucial factors: 1. Speed of dehazing operations 2. Platform-agnostic nature of the chosen algorithm. This study introduces a novel Dark Channel Prior-Low Frequency Domain (DCP-LFD) method designed to effectively and efficiently eliminate haze from single hazy images. The method employs a hybrid strategy, combining DCP for haze reduction in the low-frequency domain and Soft-thresholding for enhancing high-frequency coefficients. This dual-pronged approach enables improvement of image quality in low and high-frequency domains, contributing to better dehazing outcomes taking less computation time. The proposed method's effectiveness is accessed using metrics like PSNR and SSIM, comparing its performance against state-of-the-art dehazing methods across five benchmark datasets: SOTS, RTTS and HSTS subsets from RESIDE, HazeRD, and Middlebury datasets. No-Reference Image Quality Assessments viz. BRISQUE and FADE are performed on RTTS and private dataset to objectively evaluate dehazed image quality. A comparison of dehazing time is executed, highlighting the method's proficiency in achieving faster results. This analysis accentuates the method's capacity to efficiently remove haze from outdoor images while emphasising its time-saving efficiency.