An improved NSCT based shrinking threshold denoising algorithm for infrared image is proposed. The improved NSCT
is constructed based on the Nonseparable Wavelet Transform via non-linear lifting method with redundancy structure,
which can produce better image processing performance for its better detail capturing capability, shift invariance, multiresolution
and multi-direction. After analyzing the current threshold functions and threshold selecting methods, an novel
threshold function suitable for the improved NSCT is established with high-level continuous derivative to improve the
denosing performance for infrared image. Experimental results show that the proposed algorithm has better denoising
performance and detail-preserving capability.
A novel object tracking algorithm for FLIR imagery based on mean shift using multiple features is proposed to improve
the tracking performance. First, the appearance model of infrared object is represented in the combination of gray space,
LBP texture space, and orientation space with different feature weight. And then, the mean shift algorithm is employed to
find the object location. An on-line feature weight update mechanism is developed based on Fisher criteria, which measure
the discrimination of object and background effectively. Experiment results demonstrate the effectiveness and robustness
of the proposed method for object tracking in FLIR imagery.
Multiresolution-based image fusion has been the focus of considerable research attention in recent years with a number
of algorithms proposed. In most of the algorithms, however, the parameter configuration is usually based on experience.
This paper proposes an adaptive image fusion algorithm based on the nonsubsampled contourlet transform (NSCT),
which realizes automatic parameter adjustment and gets rid of the adverse effect caused by artificial factors. The
algorithm incorporates the quality metric of structural similarity (SSIM) into the NSCT fusion framework. The SSIM
value is calculated to assess the fused image quality, and then it is fed back to the fusion algorithm to achieve a better
fusion by directing parameters (level of decomposition and flag of decomposition direction) adjustment. Based on the
cross entropy, the local cross entropy (LCE) is constructed and used to determine an optimal choice of information
source for the fused coefficients at each scale and direction. Experimental results show that the proposed method
achieves the best fusion compared to three other methods judged on both the objective metrics and visual inspection and
exhibits robust against varying noises.