Blur detection and segmentation for a single image without any prior information is a challenging task. Numerous techniques for blur detection and segmentation have been proposed in the literature to ultimately restore the sharp images. These techniques use different blur measures in different settings, and in all of them, blur measure plays a central role among all other steps. Blur measure operators have not been analyzed comparatively for both of the spatially-variant defocus and motion blur cases. In this paper, we provide the performance analysis of the state-of-the-art blur measure operators under a unified framework for blur segmentation. A large number of blur measure operators are considered for applying on a diverse set of real blurry images affected by different types and levels of blur and noise. The initial blur maps then are segmented into blurred and non-blurred regions. In order to test the performance of blur measure operators in segmentation process in equal terms, it is crucial to consider the same intermediate steps involved in the blur segmentation process for all of the blur measure operators. The performance of the operators is evaluated by using various qualitative measures. Results reveal that the blur measure operators perform well under certain condition and factors. However, it has been observed that some operators perform adequately overall well or worse against almost all imperfections that prevail over the real-world images.

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-Data fusion of thermal and visual images is a solution to overcome the drawbacks present in individual thermal and visual images. Data fusion using different approach is discussed and results are presented in this paper. Traditional fusion approaches don't produce useful results for face recognition. An optimized approach for face data fusion is developed which works for face data fusion equally well as for non-face images. This paper presents the implementation of Human face recognition system using proposed optimized data fusion of visual and thermal images. Gabor filtering technique, which extracts facial features, is used as a face recognition technique to test the effectiveness of the fusion techniques. It has been found that by using the proposed fusion technique Gabor filter can recognize face even with variable expressions and light intensities.

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