Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This addresses video quality issues for surveillance or remote sensing applications, but it is incremental as it builds on known techniques like GMM and Kalman filtering.
The paper tackles atmospheric distortion in video sequences with moving objects by developing a recursive image fusion method using Dual Tree Complex Wavelet Transform, achieving better video quality than existing methods with competitive speed.
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.