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In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

In order to reduce the Gaussian noise introduced during image generation, this paper presents an image denoising algorithm based on variational Bayes (V-Bayes) estimation and nonsubsampled contourlet transform (NSCT) bivariate model. First, the proposed algorithm uses the NSCT's advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients. Then, the corresponding nonlinear bivariate threshold function of the model is deduced by using V-Bayes estimation theory. Finally, the noise-reduced coefficients are inverse-transformed by NSCT to obtain denoised image. The simulation results show that the denoised image has obvious improvement in subjective visual effects and performance indicators, and effectively preserves the details and texture information in the original image. [ABSTRACT FROM AUTHOR]

Streaks due to weather, such as rain or snow, degrade image quality and affect the performance of subsequential high-level vision tasks by the generated undesired artifacts. Hence, removing streak interference is an ongoing and challenging issue for many applications in realtime mobile surveillance systems. In this paper, streak interference removal from a single image is the focus. To sufficiently extract streak interference from an observed image, the image was firstly filtered with the nonsubsampled contourlet transform. Then, the residual part between the original and filtered image was decomposed into the streak component and detail component of background. Based on the additive layer model, we designed two specific priors that constrain the detail and streak interference respectively and established a model with joint priors for residual image decomposition. As a result, the resulting image can be synthesized with the filtered image and detail component. Experimental results show that our proposed method outperforms existing methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, researching the correlation property of congenetic subband coefficients of Nonsubsampled Contourlet Transform (NSCT), and proposing a new adaptive denoising theory. This theory combines the excellent shift-invariant of NSCT with the correlation property of congenetic subband coefficients in image denoising. The correlation property contains the entire relational correlation value of the congenetic subband coefficients, and the grey correlation value in single coefficient of the congenetic subband coefficients of the NSCT. According to the correlation property of congenetic subband coefficients, the new algorithm can automatically identify the strong edges, weak edges and noise in the noisy image, and then it can filter the noise and preserve the strong edges and weak edges at certain degree. The experiment results are provided to compare with the elegant non-local means method, which show that the theory proposed in this paper has good effect. [ABSTRACT FROM AUTHOR]

A multisensor image fusion approach established on the hybrid-domain image enhancement and double nonsubsampled contourlet transform (NSCT) is proposed. The hybrid-domain pre-enhancement algorithm can promote the contrast of the visible color image. Different fusion rules are, respectively, selected and applied to obtain fusion results. The double NSCT framework is introduced to obtain better fusion performance than the general single NSCT framework. Experimental outcomes in fused images and performance results demonstrate that the presented approach is apparently more advantageous. [ABSTRACT FROM AUTHOR]

Over the past decades, an increasing number of local feature descriptors have been proposed in the community of computer vision and pattern recognition. Although they have achieved impressive results in many applications, how to find a balance between accuracy and computational efficiency is still an open issue. To address this issue, we present a local feature descriptor using entropy rate (FDER), which is robust to a variety of image transformations. We first employ the nonsubsampled Contourlet transform to produce multiple support regions and design a graph structure to describe the sub-region. We then use the entropy rate of random walks on the designed graph to build the FDER descriptor. Extensive experiments demonstrate the superiority of proposed descriptor dealing with various image transformations in comparison with the existing state-of-the-art descriptors. [ABSTRACT FROM AUTHOR]

We present a multiexposure image fusion method that can enhance details, yet effectively improve brightness in the final result. In addition, a valid weight measurement is developed to remove motion objects in dynamic scenes. During the fusion process, each source image is first decomposed into one low-pass sub-band and a series of high-pass directional sub-bands using the nonsubsampled contourlet transform. Then the blended sub-bands are constructed by weight maps of the source images. To preserve the details of source images and adjust brightness of final image, gain control maps are used for each fused sub-band. Experimental results demonstrate that the proposed method significantly outperforms the traditional methods in terms of both visual inspection and objective evaluation, especially in cases which the regions of interest are in dark areas. [ABSTRACT FROM AUTHOR]

On fusing infrared and visible image, the traditional fusion method cannot get the better image quality. Based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain, the fusion algorithm was proposed. Firstly, NSCT was adopted to decompose infrared and visible images at different scales and directions for the low and high frequency coefficients, the low frequency coefficients which were fused with improving regional weighted fusion method based on neighborhood energy, and the high-frequency coefficients were fused with multi-judgment rule based on neighborhood characteristic regional process. Finally, the coefficients were reconstructed to obtain the fused image. The experimental results show that, compared with the other three related methods, the proposed method can get the biggest value of IE (information entropy), MI(VI, F ) (mutual information from visible image), MI(VI, F ) (mutual information from infrared image), MI (sum of mutual information), and Q AB / F (edge retention). The proposed method can leave enough information in the original images and its details, and the fused images have better visual effects. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an automatic enhancement method for SAR images based on the nonsubsampled contourlet transform (NSCT) and the memetic algorithm (MA). Firstly, an improved enhancement function which integrates the speckle reduction with the feature enhancement is proposed to nonlinearly shrink and stretch the NSCT coefficients, and then an multi-population cooperative MA (MP-CMA) is presented to automatically adjust the parameters of the enhancement function. We propose an objective criterion for enhancement, and attempt finding the (near) optimal image according to the enhancement criterion. We employ the MP-CMA as a global search strategy for the best enhancement image which has a satisfactory compromise between sharpening and smoothing. The experimental results show that the proposed method can efficiently enhance the edge features and contrast of SAR images and reduce the speckle noises and outperforms the wavelet-based and NSCT-based non-automatic enhancement methods. [Copyright &y& Elsevier]

Highlights: [•] Relationship between inpainting and the nonsubsampled contourlet transform. [•] Use of information priority to fill in the target patch. [•] Automatic selection of the target region. [•] Keeping the transients of the original image. [Copyright &y& Elsevier]

The optimization of image fusion is researched. Based on the properties of nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In order to meet the requirement of feedback optimization, a new image fusion quality metric of image quality index normalized edge association (IQI-NEA) is built. A polynomial model is adopted to establish the relationship between the IQI_NEA metric and several decomposition levels. The optimal fusion includes four steps. First, the source images are decomposed in NSCT domain for several given levels. Second, principal component analysis is adopted to fuse the low frequency coefficients and the maximum fusion rule is utilized to fuse the high frequency coefficients to obtain the fused coefficients and the fused result is reconstructed from the obtained fused coefficients. Third, calculate the fusion quality metric IQI_NEA for the source images and fused images. Finally, the optimal fused image and optimal level are obtained through extremum properties of polynomials function. The visual and statistical results show that the proposed method has optimized the fusion performance compared to the existing fusion schemes, in terms of the visual effects and quantitative fusion evaluation indexes. [ABSTRACT FROM AUTHOR]

Abstract: An novel edge detection approach is proposed. Firstly, the nonsubsampled contourlet transform is used to decompose the original image into low frequency approximation image and the high frequency subbands. Then, because of nonsubsampled contourlet transform shift-invariance, the original image corresponding gradient magnitude is redefined in every scale. Finally, different scales gradient is synthesized into the image dege. The experimental results of edge detection for several test images are provided to demonstrate the approach. [Copyright &y& Elsevier]

Abstract: In this paper, an improved image denoising method based on a nonlinear thresholding function with adaptive bayes threshold in Nonsubsampled Contourlet Transform (NSCT) domain. In overcoming the shortcomings of the same threshold, the noise deviation of the different sub-band are estimated based on the coefficients of different directional and level sub-bands in NSCT domain, and the thresholds of every sub-band is estimated by Bayesian threshold estimation method. After choosing the thresholds, a nonlinear thresholding function was chosen to overcome the shortcomings of the soft and the hard thresholding function. The simulation results show that the proposed method in this paper can remove Gaussian white noise more effectively, and get a higher PSNR value and keep image texture and detail information more clearly, which also has a better visual effect. [Copyright &y& Elsevier]

Abstract: Face recognition under varying lighting conditions is challenging, especially for single image based recognition system. Exacting illumination invariant features is an effective approach to solve this problem. However, existing methods are hard to extract both multi-scale and multi-directivity geometrical structures at the same time, which is important for capturing the intrinsic features of a face image. In this paper, we propose to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform. LNSCT can extract strong edges, weak edges, and noise from a face image using NSCT in the logarithm domain. We analyze that in the logarithm domain the low-pass subband of a face image and the low frequency part of strong edges can be regarded as the illumination effects, while the weak edges and the high frequency part of strong edges can be considered as the reflectance component. Moreover, even though a face image is polluted by noise (in particular the multiplicative noise), the reflectance component can still be well estimated and meanwhile the noise is removed. The LNSCT can be applied flexibly as neither assumption on lighting condition nor information about 3D shape is required. Experimental results show the promising performance of LNSCT for face recognition on Extended Yale B and CMU-PIE databases. [Copyright &y& Elsevier]