Image Dehazing Algorithm Based on Cell Vibration Energy Model

Image dehazing is a challenging task in image enhancement. Due to the widespread presence of haze, it obscures scenes leading to loss of image features, and consequently, failure of visual tasks. To address this issue, this paper proposes a fast and effective image dehizing algorithm based on the cell vibration energy model and image enhancement ideas. Specifically, first, an optimized global enhancement model based on cell vibration is proposed to further improve the computational efficiency of the enhancement process. Then, inspired by image enhancement ideas, a two-round enhancement dehazing framework using stacked modules is proposed. Following that, a negative lightness difference image estimation method combining mean filtering, fast guided filtering, and linear transformation is proposed to solve the core parameter estimation problem within the framework. At the same time, a value range constraint strategy is proposed to limit the value range of the negative lightness map and enhance the adaptability of the algorithm. Lastly, a proportion adjustment method is used to further adjust the brightness of the dehazed image. Experimental results show that the proposed dehazing algorithm outperforms nine state-of-the-art methods in terms of dehazing efficiency and detail restoration.