Image edge detection based on nonsubsampled contourlet transform and mathematical morphology

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Directional Filter for SAR Images Based on Nonsubsampled Contourlet Transform and Immune Clonal Selection

A directional filter algorithm for intensity synthetic aperture radar （SAR） image based on nonsubsampled contourlet transform （NSCT） and immune clonal selection （ICS） is presented. The proposed filter mainly focuses on exploiting different features of edges and noises by NSCT. Furthermore, ICS strategy is introduced to optimize threshold parameter and amplify parameter adaptively. Numerical experiments on real SAR images show that there are improvements in both visual effects and objective indexes.

Infrared and Visible Image Fusion Based on Region of Interest Detection and Nonsubsampled Contourlet Transform

In order to enhance the contrast of the fused image and reduce the loss of fine details in the process of image fusion,a novel fusion algorithm of infrared and visible images is proposed.First of all,regions of interest(RoIs)are detected in two original images by using saliency map.Then,nonsubsampled contourlet transform(NSCT)on both the infrared image and the visible image is performed to get a low-frequency sub-band and a certain amount of high-frequency sub-bands.Subsequently,the coefcients of all sub-bands are classified into four categories based on the result of RoI detection:the region of interest in the low-frequency sub-band(LSRoI),the region of interest in the high-frequency sub-band(HSRoI),the region of non-interest in the low-frequency sub-band(LSNRoI)and the region of non-interest in the high-frequency sub-band(HSNRoI).Fusion rules are customized for each kind of coefcients and fused image is achieved by performing the inverse NSCT to the fused coefcients.Experimental results show that the fusion scheme proposed in this paper achieves better efect than the other fusion algorithms both in visual efect and quantitative metrics.

Image Fusion Scheme Based on Nonsubsampled Contourlet and Block-Based Cosine Transform

A kind of nonsubsampled contourlet and block-based cosine transform(NSCBCT)is developed,and its application in image fusion is studied in this paper.The construction of filtering banks is based on the nonsubsampled contourlet transform(NSCT)and block-based discrete cosine transform(B-DCT).We combine NSCT and B-DCT to design filters that lead to NSCBCT with better singularity representation than either of them in isolation.A design framework based on the hybrid approach is proposed,which allows for the fast implementation based on NSCT and B-DCT respectively.In addition,a new image fusion scheme based on NSCBCT for multispectral and panchromatic satellite images is proposed.Firstly,because it adopts NSCT,the fused satellite images have higher spatial resolution than those based on wavelets.Secondly,based on the localized high frequency information provided by B-DCT,the proposed fusion scheme can reduce the spectral distortion of the fused image further.Experimental results show that the proposed fusion method is able to increase the spatial resolution and reduce the spectral distortion of the fused image at the same time.

Fusion of Remote Sensing Images Based on Nonsubsampled Contourlet Transform and Region Segmentation

The purpose of remote sensing images fusion is to produce a fused image that contains more clear,accurate and comprehensive information than any single image.A novel fusion method is proposed in this paper based on nonsubsampled contourlet transform(NSCT) and region segmentation.Firstly,the multispectral image is transformed to intensity-hue-saturation(IHS) system.Secondly,the panchromatic image and the component intensity of the multispectral image are decomposed by NSCT.Then the NSCT coefficients of high and low frequency subbands are fused by different rules,respectively.For the high frequency subbands,the fusion rules are also unalike in the smooth and edge regions.The two regions are segregated in the panchromatic image,and the segmentation is based on particle swarm optimization.Finally,the fusion image can be obtained by performing inverse NSCT and inverse IHS transform.The experimental results are evaluated by both subjective and objective criteria.It is shown that the proposed method can obtain superior results to others.

Improved image enhancement method for flotation froth image based on parameter extraction

Froth image could strongly indicate the production status in mineral flotation process.Considering low contrast and sensitivity to noises and illumination of froth images in flotation cells,an improved image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) and multiscale Retinex algorithm has been proposed.Nonsubsampled contourlet transform was firstly adopted to decompose the flotation froth images,ensure signals invariance and avoid the blurring edge.Secondly,a multiscale Retinex algorithm was used to enhance the lower frequency image and improve the brightness uniformity.Adaptive classification method based on Bayes atrophy threshold was proposed to eliminate noise,preserve strong edges,and enhance weak edges of band-pass sub-band images.Experiment shows that the proposed method could enhance the edge,contour,details and curb noise,and improve visual effects.Under-segmentation caused by noise and blurring edge has been solved,which lays a foundation for extracting foamy morphological flotation froth and analyzing grade.

Noise Reduction of Welding Defect Image Based on NSCT and Anisotropic Diffusion

In order to reduce noise effectively in the welding defect image and preserve the minutiae information, a noise reduction method of welding defect image based on nonsubsampled contourlet transform(NSCT) and anisotropic diffusion is proposed. Firstly, an X-ray welding defect image is decomposed by NSCT. Then total variation(TV) model and Catte_PM model are used for the obtained low-pass component and band-pass components, respectively. Finally, the denoised image is synthesized by inverse NSCT. Experimental results show that, compared with the hybrid method of wavelet threshold shrinkage with TV diffusion, the method combining NSCT with P_Laplace diffusion, and the method combining contourlet with TV model and adaptive contrast diffusion, the proposed method has a great improvement in the aspects of subjective visual effect, peak signal-to-noise ratio(PSNR) and mean-square error(MSE). Noise is suppressed more effectively and the minutiae information is preserved better in the image.

A Novel Algorithm for Microcirculation Image Enhancement

Microcirculation images often have low quality in acquisition process, which affect the following steps of process. This paper introduces enhancement algorithm based on nonsubsampled Contourlet transform (NSCT). It analyzes the characteristics of the microcirculation images generated, and separates microcirculation images to light weight and the reflection weight. It also analyzes the construction method on NSCT and proves that this method can be applied on microcirculation image enhancement algorithm. To correct light weight of microcirculation image and obtain enhancement image the enhancement microcirculation image was not only superior to the original image visually, but also improved objective data obviously. The algorithms provide a new method to microcirculation image pre-processing and guide the latter steps of the image processing.