Turbulent-Degraded Image Restoration Via Improved Principal Component Analysis Method

It is challenging to removing atmospheric turbulence-induced geometric distortion and blurry degradation simultaneously. The improved principal component analysis (i-PCA) method is proposed in this paper. The image set selection strategy is implemented by the Euclidean distance between each sub-module image and its corresponding reference image to remove large geometric deformations and blurs directly. The eigenvector representing the original image data is uniquely determined by combining the maximum correlation with the original image and the maximum variance of the corresponding principal component for resolving the direction issue of the eigenvector. The algorithm is tested using synthetic and real turbulence-distorted images. The experimental results show that the i-PCA method proposed can effectively alleviate the atmospheric turbulence effects and significantly enhance visual quality, promising for its application to turbulence-distorted image.