Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

Image denoising process based on Contourlet transforming

Abstract: The invention discloses an image denoising method which is based on Contourlet transformation, and belongs to the field of image processing. The realization process of the method comprises the following steps: firstly, carrying out the cycle-spinning of a noisy image so as to obtain a plurality of panning images of the noisy image; then, carrying out the Contourlet transformation of the panning images and optimizing the Contourlet transformation coefficient; then carrying out the Contourlet inverse transformation of the optimized Contourlet coefficient so as to obtain a plurality of panning images of the de-noised noisy image; carrying out the reverse cycle-spinning of the images; and then averaging the images so as to obtain the final de-noised image of the noisy image. The method which utilizes the orientation information-capturing characteristic of Contourlet has the advantages that the fine texture and the edge information of the image can be better reserved as well as the noise can be effectively suppressed by distinguishing the edges and the noise of the noisy image through the threshold method; and the distortion generated on the de-noised image can be effectively eliminated by the cycle-spinning process of the noisy image. Compared with few de-noising methods, the denoising method of the invention further has the advantages of highest PSNR value and optimal de-noising effect.

Car type recognition in highways based on wavelet and contourlet feature extraction

Abstract: Recently many works focus on the vehicle type recognition because it is important in security and authentication systems. Computational complexity and low recognition rate especially when the system has to recognize among a large number of vehicles, are two major problems in vehicle type recognition. In recent years wavelet and contourlet transform have been applied in the recognition tasks successfully. In this paper we proposed a method for recognizing vehicle type in different lighting conditions. We used wavelet and contourlet as tools for feature extraction. These features are powerful and robust to illumination and scale variation. We reduced the dimension of feature vector by resizing the wavelet and contourlet subbands and then applied normalization on those coefficients. Our method is robust to a few variations in vehicle frontal view angels and distance to camera. The experimental results showed 97.35% true recognition rate for 14 classes of cars which is a significant increase for vehicle type recognition.

Image Enhancement Based on the Nonsubsampled Contourlet Transform and Adaptive Threshold

Abstract: An algorithm for image enhancement based on the nonsubsampled contourlet transform and adaptive threshold is proposed.The coefficients in different scales and different directions are obtained by image decomposition using the nonsubsampled contourlet transform.With these coefficients,thresholds and the enhancement functions are adaptively set.After the enhancement and then reconstruction of these coefficients,image enhancement is implemented.Compared with other algorithms,this algorithm can get better effect.

Artifact-Free Despeckling of SAR Images Using Contourlet

Abstract: Contourlets have been attracting increasing attention in despeckling of synthetic aperture radar (SAR) images in recent years. However, contourlets produce undesirable artifacts while despeckling. This letter presents a novel contourlet-based regularization method to remove speckle without introducing extra artifacts. In this method, a nonlocal regression function is constructed for the regularization term through patch weighting on the contourlet reconstruction. Moreover, by employing the intrinsic dependence of contourlet coefficients, a parameterized shrinkage algorithm is proposed to resolve the contourlet reconstruction. This contourlet despeckling method enables significant elimination of speckle and resultant artifacts with well-protected strong scatters and structures of the scene. Experiments demonstrate such superiority on real SAR images.