This paper proposed a novel filter scheme by image fusion based on Nonsubsampled ContourletTransform(NSCT) for multispectral image. Firstly, an adaptive median filter is proposed which shows great advantage in speed and weak edge preserving. Secondly, the algorithm put bilateral filter and adaptive median filter on image respectively and gets two denoised images. Then perform NSCT multi-scale decomposition on the de-noised images and get detail sub-band and approximate sub-band. Thirdly, the detail sub-band and approximate sub-band are fused respectively. Finally, the object image is obtained by inverse NSCT. Simulation results show that the method has strong adaptability to deal with the textural images. And it can suppress noise effectively and preserve the image details. This algorithm has better filter performance than the Bilateral filter standard and median filter and theirs improved algorithms for different noise ratio.
A new target extraction algorithm based Nonsubsampled Contourlet Transform(NSCT) is proposed according to the difficulty of weak target extraction. Paper detailed analyses and summarizes the data feature of image in NSCT domain, proposes a weak target extraction algorithm using high-frequency coefficients and mathematical morphology. The high-frequency coefficients were calculated by NSCT at first. Then the high-frequency coefficients were processed for noise cancellation by adaptive thresholds in corresponding sub-band. After these operations, the mathematical morphology method was adopted to remedy the defects of image contour. Finally, the object image is obtained by inverse NSCT. The simulation results show that this method can detect the target information fast and accurately, can meet the practical requirement.
A no-reference image quality assessment method for super-resolution reconstruction is proposed. The basic idea is to
perform a contourlet multiscale decomposition of low resolution image and reconstructed super resolution image first.
According to the relativity of the contourlet coefficient, the reconstructed image is divided into sharp edges, image
texture and flat region. Then, calculate the ringing intensity index of sharp edges, the blur extent index of the image
texture and the directional entropy index of the high frequency components. Finally, the result to evaluate the
reconstructed image quality is obtained by integrated these indexes into one total image quality index. Several
experimental results using simulated images demonstrate the new index is efficient and stable for evaluating the quality
of the reconstructed super-resolution image. It performs well in accordance with human subjective vision.