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This thesis proposes new techniques as well as considers state-of-the-art and recently developed methods to analyse images acquired through different turbulent media, such as atmospheric and underwater turbulence. The techniques include some challenging image processing tasks, such as image restoration, object detection and identification. In particular, it deals with both space-variant and space-invariant blur removal and supress the distortions. In addition, two effective techniques are proposed for object detection in images acquired through long-range atmospheric turbulent path, and object identification in images acquired through underwater turbulent media. Possible applications of these techniques can include different forms, such as military surveillance, detecting and identifying objects, imaging problems in remote sensing, and various ocean applications. A faster image pre-processing technique by employing the k-means clustering technique is proposed in this thesis for selecting the important frames rather than considering random frames. This technique is used in imaging for both turbulent media. A recent restoration method is reviewed and improved by employing a spatio-temporal kernel regression based fusion method for removing space-invariant blur from the images and supress deformations. To further improve the image quality a blind deconvolution procedure is employed. In practice, the images acquired through turbulent media are distorted due to space-variant blur problem. In-order to address this problem a patch-wise deconvolution process is carried out on the distorted image, and the turbulence corrected image is given as input to the dictionary learning algorithm for further denoising. Motivated by the saliency technique, object detection technique is proposed considering the small cluttered objects present in the images acquired. These objects makes other detection problems difficult such as moving object detection. Thus, it is important to detect the small cluttered objects. This thesis also focuses on the imaging issues considering underwater turbulence media. Images are distorted mainly due to non-uniform illumination that occurs as a result of scattering and absorption of various submerged organic substances, and space-variant blur. The Retinex model is employed to correct the non-uniform illumination problem prior to space-variant blur removal. A patch-wise operation is then carried out for further restoration purpose. Image segmentation algorithm by considering the efficiency of suppression factor is then implemented on the restored image for identifying the objects. The potential and the performance of the proposed approaches are investigated considering both real-world and synthetic datasets, and shows better restoration, detection and identification results. The results show practical interest for considering the proposed approaches in different computer vision and image processing systems, and machine learning and pattern analysis applications. |
