Journal Article

Single Image Haze Removal via Multiple Variational Constraints for Vision Sensor Enhancement

Zhao, Weijia, Liu, Hongyu, Liu, Yun, Feng, Yuxue, Li, Yuxiao, Wang, Luyao

2025

Overview

Images captured by vision sensors in outdoor environments often suffer from haze-induced degradations, including blurred details, faded colors, and reduced visibility, which severely impair the performance of sensing and perception systems. To address this issue, we propose a haze-removal algorithm for hazy images using multiple variational constraints. Based on the classic atmospheric scattering model, a mixed variational framework is presented that incorporates three regularization terms for the transmission map and scene radiance. Concretely, an ℓp norm and an ℓ2 norm were constructed to jointly enforce the transmissions for smoothing the details and preserving the structures, and a weighted ℓ1 norm was devised to constrain the scene radiance for suppressing the noises. Furthermore, our devised weight function takes into account both the local variances and the gradients of the scene radiance, which adaptively perceives the textures and structures and controls the smoothness in the process of image restoration. To address the mixed variational model, a re-weighted least square strategy was employed to iteratively solve two separated subproblems. Finally, a gamma correction was applied to adjust the overall brightness, yielding the final recovered result. Extensive comparisons with state-of-the-art methods demonstrated that our proposed algorithm produces visually satisfactory results with a superior clarity and vibrant colors. In addition, our proposed algorithm demonstrated a superior generalization to diverse degradation scenarios, including low-light and remote sensing hazy images, and it effectively improved the performance of high-level vision tasks.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: Algorithms / atmospheric scattering model / Deep learning / haze removal / Light / Medical imaging equipment / mixed variational model / Norms / Optimization / Remote sensing / scene recovery / Sensors / Sparsity / vision sensors