Dimensional Transformation Mixer for Ultra-High-Definition Industrial Camera Dehazing

Haze severely affects the reliability of vision-based industrial systems, and most of the current dehazing methods are not applicable to images captured by ultra-high-definition (UHD) industrial cameras. In this article, we propose a novel dimensional transformation mixer (DMixer) model for recovering haze-free images from UHD haze images. In DMixer, the dimensional transformation module encodes the complete image in multiple stages from different perspectives and associates features from different views by permuting the tensor for efficient long-range dependency modeling. In this way, the global perception capabilities of DMixer are complemented, allowing the quality of the reconstructed UHD images to be improved. Furthermore, DMixer employs a dual-stream network framework that combines local and multiscale features, allowing DMixer to better trade off the performance and efficiency for real industrial systems. Our proposed method enables the real-time processing of UHD images (
$\sim$
59 fps). Extensive results show that the proposed model outperforms current state-of-the-art methods, with PSNR improvements of 1.94 and 0.95 dB on the 4KID and O-HAZE datasets, respectively.