Contourlet

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

Pan-sharpening via multi-scale and multiple deep neural networks

Abstract: Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.

Punched workpiece defect detection method based on image processing

Abstract: The present invention discloses a punched workpiece defect detection method based on image processing. The method comprises the concrete steps of obtaining a punched workpiece image and using an adaptive voting fast median filtering method to carry out image denoising processing, using Contourlet transform and a niche particle swarm optimization algorithm to carry out image enhancement and carrying out edge detection processing on the image, and finally carrying out punched workpiece defect detection. According to the method, the Contourlet transform and the niche particle swarm optimization algorithm are used to carry out image enhancement, the image overall contrast is raised, the edge detail of the workpiece image is enhanced, the repeatability of the workpiece detection is increased, for the characteristic that the defect part edge in the punched workpiece image is obvious, the invention provides an edge detection based on the integration of a neural network and a rapid fuzzy algorithm, and while the detection cost is reduced, the detection efficiency is greatly improved.

Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Abstract: In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NSCT. Secondly, contrast stretching is adopted to deal with the low-frequency sub-band coefficients, and the non-local means filter is applied to suppress the noise contained in the first high-frequency sub-band coefficients. Thirdly, the processed coefficients are reconstructed with the inverse NSCT transform. Finally, the unsharp filter is used to enhance the details of the image. The simulation results show that the proposed algorithm has better performance in remote sensing image enhancement than the existing approaches.

Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background.

Abstract: Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

A comparative study on mammographic image denoising technique using wavelet, curvelet and contourlet transforms

Abstract: This article focuses on comparing the discriminating power of the various multi-resolution based thresholding techniques - wavelet, curvelet, and contourlet for image denoising Using multiresolution techniques, mammogram images are decomposed into different resolution levels, which are sensitive to different frequency bands We implement the proposed algorithm on the mammogram images embedded in Random, Salt and Pepper, Poisson, Speckle and Gaussian noises Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet and contourlet transform methods The curvelet transform has a strong directional character which combines multiscale analysis and ideas of geometry to achieve the optimal rate of convergence by simple thresholding The proposed algorithm succeeded in providing improved denoising performance to recover the shape of edges and important detailed components Empirical results proved that the curvelet-based thresholding can obtain a better image estimate than the wavelet- based and contourlet-based restoration methods