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Publication - Professor David Bull

    Atmospheric Turbulence Mitigation for Sequences with Moving Objects Using Recursive Image Fusion

    Citation

    Anantrasirichai, P, Achim, A & Bull, D, 2018, ‘Atmospheric Turbulence Mitigation for Sequences with Moving Objects Using Recursive Image Fusion’. in: 2018 25th IEEE International Conference on Image Processing (ICIP 2018). Institute of Electrical and Electronics Engineers (IEEE)

    Abstract

    This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and tracked using the improved Gaussian mixture models (GMM) and Kalman filtering. New fusion rules are introduced which work on the magnitudes and angles of the DT-CWT coefficients independently to achieve a sharp image and to reduce atmospheric distortion, respectively. The subjective results show that the proposed method achieves better video quality than other existing methods with competitive speed.

    Full details in the University publications repository