Abstract
This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.
Highlights
Weather conditions can severely limit visibility in outdoor scenes
We present in detail our proposed method, which uses the recurrent fuzzy cerebellar model articulation controller (RFCMAC) model and a weighted strategy to recover scenes from the removal of a hazy image
This study proposes an RFCMAC model for estimating the transmission map more accurately
Summary
Weather conditions can severely limit visibility in outdoor scenes. In such cases, atmospheric phenomena such as fog and haze will significantly degrade visibility in the captured scene. Since visibility is dependent on the air, the amount of particles in the air will affect image visibility This phenomenon is generally composed of water droplets or particles and cannot be ignored. The low visibility in hazy images affects the accuracy of computer vision techniques, such as object detection, face tracking, license plate recognition, satellite imaging, and so on, as well as multimedia devices, such as surveillance systems and advanced driver assistance systems. Proposed strategies for enhancing the visibility of a degraded image include the following
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