Foggy image enhancement based on Principal Component Analysis

Foggy image enhancement is an important branch of digital image processing, which significantly benefits traffic and outdoor visual systems. To overcome the shortcomings of the existing foggy image enhancement algorithms, we have developed a method that combines Principal Component Analysis (PCA), Multi-Scale Retinex (MSR) and Global Histogram Equalization (GHE). Initially, a PCA transform is applied to the foggy image to split the input image into a luminance and two chrominance components. In the second step, the luminance and the chrominance components are individually enhanced by MSR and GHE, respectively. In the final stage, an inverse PCA is applied to combine the results of the three channels into a new RGB image. Experimental results show that the proposed method can effectively be used to remove the image degradation captured in foggy weather and enhance the sharpness of the image.