Weisheng Li

Single image haze removal based on haze physical characteristics and adaptive sky region detection

Outdoor images are often degraded by haze and other inclement weather conditions, which affect both consumer photographs and computer vision applications severely. Therefore, researchers have proposed plenty of restoration approaches to deal with this problem. However, it is hard to tackle the color distortion problem in restored images with ignoring the differences between fog and haze. Meanwhile, the atmospheric light is also an important variable that influences the global illumination of images. In this paper, we analyze the physical meaning of atmospheric light first, and estimate atmospheric light by a novel method of obtaining the sky region in images, which is based on our newly proposed sky region prior. Then after exploring physical characteristics of fog and haze, we explain why images taken in haze appear yellowish, and eliminate this phenomenon by our adaptive channel equalization method. Quantitative comparisons with seven state-of-art algorithms on a variety of real-world haze images demonstrate that our algorithm can remove haze effectively and keep color fidelity better. HighlightsWe propose a novel single image haze removal approach.Our approach is based on haze physical character and adaptive sky region detection.We analyze the haze physical character and meaning of atmospheric scattering model.We propose a sky region prior based on thousands of outdoor images.We propose a new adaptive sky region detection method to estimate atmospheric light.We eliminate distortion in hazy images by our adaptive channel equalization method.We convert to HSI color space to compensate over-saturation phenomenon in RGB space.

Union Laplacian pyramid with multiple features for medical image fusion

The Laplacian pyramid has been widely used for decomposing images into multiple scales. However, the Laplacian pyramid is believed as being unable to represent outline and contrast of the images well. To tackle these tasks, an approach union Laplacian pyramid with multiple features is presented for accurately transferring salient features from the input medical images into a single fused image. Firstly, the input images are transformed into their multi-scale representations by Laplacian pyramid. Secondly, the contrast feature map and outline feature map are extracted from the images at each scale, respectively. Thirdly, after extracting the multiple features, an efficient fusion scheme is developed to combine the pyramid coefficients. Lastly, the fused image is obtained by a reconstruction process of the inversed pyramid. Visual and statistical analyses show that the quality of fused image can be significantly improved over that of typical image quality assessment metrics in terms of structural similarity, peak-signal-to-noise ratio, standard deviation, and tone mapped image quality index metrics. The contrast is also well preserved by histogram analysis of images. Affine transformation is introduced in pyramid achieving multi-orientations.Kirsch method is used to highlight the contrast of the images.PCA method is for highlighting the contrast of the images.The averaging different orientation is to preserve the structure.Histogram of images in experimental part is to evaluate contrast.

Radial shifted Legendre moments for image analysis and invariant image recognition

The rotation, scaling and translation invariant property of image moments has a high significance in image recognition. Legendre moments as a classical orthogonal moment have been widely used in image analysis and recognition. Since Legendre moments are defined in Cartesian coordinate, the rotation invariance is difficult to achieve. In this paper, we first derive two types of transformed Legendre polynomial: substituted and weighted radial shifted Legendre polynomials. Based on these two types of polynomials, two radial orthogonal moments, named substituted radial shifted Legendre moments and weighted radial shifted Legendre moments (SRSLMs and WRSLMs) are proposed. The proposed moments are orthogonal in polar coordinate domain and can be thought as generalized and orthogonalized complex moments. They have better image reconstruction performance, lower information redundancy and higher noise robustness than the existing radial orthogonal moments. At last, a mathematical framework for obtaining the rotation, scaling and translation invariants of these two types of radial shifted Legendre moments is provided. Theoretical and experimental results show the superiority of the proposed methods in terms of image reconstruction capability and invariant recognition accuracy under both noisy and noise-free conditions. Two types of radial Legendre moments for image analysis are proposed.The proposed moments are transformed Legendre moments.These proposed moments can be thought as orthogonalized complex moments.A framework of deriving RST invariance of the transformed moments is investigated.The proposed invariants are robust to additive white noise.

Object recognition based on the Region of Interest and optimal Bag of Words model

Bag of Words (BoW) model has been widely used in conventional object recognition tasks. Different from the existing methods, this paper proposed a method for object recognition based on Region of Interest (ROI) and Optimal Bag of Words model. It includes the following steps: (1) ROI extraction in combination with the Shi-Tomasi corner and Itti saliency map; (2) The SIFT feature descriptors are detected and described on images of interest; (3) A visual codebook is generated through utilizing the Gaussian mixture models, which relies on the clustering results of k-means++; (4) The similarities between each visual word and corresponding local feature are computed by posterior pseudo probabilities discriminative to construct a visual word soft histogram for image representation; (5) The Support vector machine (SVM) is used to perform image classification and recognition. The experiments are performed on the MSRC 21-class database. The results show that the proposed method can be more accurately recognize images.

Lossless image compression based on integer Discrete Tchebichef Transform

Transform coding plays a very important role in image and video compression. Discrete Cosine Transform (DCT) is used as standard scheme (i.e. JPEG) in lossy image compression. Consequently, integer Discrete Cosine Transform (iDCT) is presented to achieve lossless compression for the compatibility of JPEG. Presently, with the investigation of new and well performed image transform techniques, there is an undeniable need for novel transform coding technologies to improve the compression rates and reduce computational complexity in the field of transform based lossless image compression. Discrete Tchebichef Transform (DTT) is a potentially unexploited orthogonal transform, and has shown a number of valuable properties like energy compaction and recursive computation. It has been preliminarily introduced in lossy image compression and shown the superiority in the compression rates. However, the DTT has not been investigated in lossless image compression. In this paper, we study DTT and matrix factorization theory firstly, and then factorize the N×N DTT matrix into N+1 single-row elementary reversible matrices (SERMs) with minimum rounding errors. On this base, we introduce a novel algorithm, named integer DTT (iDTT), to achieve integer to integer mapping for efficient lossless image compression. A series of experiments are carried out and results show that the proposed iDTT algorithm not only has higher compression ratio than iDCT method, but also is compatible with the widely used JPEG standard. A framework of lossless image compression based on integer DTT is proposed.The compression efficiency of integer DTT is higher than integer DCT.The proposed lossless image compression scheme is compatible with JPEG.Both lossy and lossless image compression can be realized under the proposed scheme.A lossless color transform method based on traditional RGB to YCbCr is presented.

Explicit Krawtchouk moment invariants for invariant image recognition

Abstract. The existing Krawtchouk moment invariants are derived by a linear combination of geometric moment invariants. This indirect method cannot achieve perfect performance in rotation, scale, and translation (RST) invariant image recognition since the derivation of these invariants are not built on Krawtchouk polynomials. A direct method to derive RST invariants from Krawtchouk moments, named explicit Krawtchouk moment invariants, is proposed. The proposed method drives Krawtchouk moment invariants by algebraically eliminating the distorted (i.e., rotated, scaled, and translated) factor contained in the Krawtchouk moments of distorted image. Experimental results show that, compared with the indirect methods, the proposed approach can significantly improve the performance in terms of recognition accuracy and noise robustness.

Moments and moment invariants in the Radon space

Radon transform has been acknowledged as the promising solution for image processing due to its high noise robustness and the ability of converting the rotation, scaling and translation operations on a pattern image into translations and scaling in the Radon image. Recently, several transforms widely employed in signal processing have been introduced in images? Radon space for pattern recognition. However, moments and especially moment invariants in the Radon space have not been thoroughly investigated. In this paper, we introduce a mathematical framework of constructing moments and moment invariants in the Radon space. First, rotational moments which represent non-orthogonal moments and Legendre-Fourier moments which represent orthogonal moments are introduced in the Radon space respectively. On this basis, we propose a method to obtain rotation, scaling and translation as well as affine invariance of these moments in the Radon space. Second, we prove that the proposed moments in the Radon space can be represented by a linear combination of classical geometric moments. With this property, the implementation time of the moments in the Radon space can be significantly reduced, and the recognition accuracy can also be greatly improved since no numerical approximation is involved. Theoretical and experimental analysis on invariant recognition accuracy, noise robustness, image blur distortion and computational time also shows the superiority of the proposed methods. HighlightsA framework of constructing moments and moment invariants in the Radon space is introduced.A method to obtain RST and affine invariance of moments in the Radon space is proposed.The implementation time of the proposed moments can be significantly reduced.The recognition accuracy of the proposed moments can also be increased.

Errata and comments on Orthogonal moments based on exponent functions

In this paper, we show that the Exponent-Fourier moments (EFMs), as defined in Ref. 1] are imprecise and the radial function of EFMs performs numerically unstable. At last, an improvement of Exponent-Fourier moments (IEFMs) is given for avoiding the above problems. Experimental results also show the superiority of the proposed methods. The imprecisions of Exponent-Fourier moments (EFMs) are pointed out.An improvement of Exponent-Fourier moments (IEFMs) is proposed.The IEFMs avoid the imprecisions of EFMs and performs much better than EFMs.

The Recognition of the Point Symbols in the Scanned Topographic Maps

It is difficult to separate the point symbols from the scanned topographic maps accurately, which brings challenges for the recognition of the point symbols. In this paper, based on the framework of generalized Hough transform (GHT), we propose a new algorithm, which is named shear line segment GHT (SLS-GHT), to recognize the point symbols directly in the scanned topographic maps. SLS-GHT combines the line segment GHT (LS-GHT) and the shear transformation. On the one hand, LS-GHT is proposed to represent the features of the point symbols more completely. Its R-table has double level indices, the first one is the color information of the point symbols, and the other is the slope of the line segment connected a pair of the skeleton points. On the other hand, the shear transformation is introduced to increase the directional features of the point symbols; it can make up for the directional limitation of LS-GHT indirectly. In this way, the point symbols are detected in a series of the sheared maps by LS-GHT, and the final optimal coordinates of the setpoints are gotten from a series of the recognition results. SLS-GHT detects the point symbols directly in the scanned topographic maps, totally different from the traditional pattern of extraction before recognition. Moreover, several experiments demonstrate that the proposed method allows improved recognition in complex scenes than the existing methods.

Brightness and contrast controllable image enhancement based on histogram specification

Abstract Histogram based image enhancement techniques are widely used for performing contrast enhancement in images. However, most histogram based image enhancement methods have insufficient capability to freely tune the brightness and contrast of enhanced image. In this paper, two novel histogram based image enhancement algorithms are proposed. The proposed algorithms provide the way to control the brightness and contrast of enhanced image by adjusting two parameters. The principles for parameter selection are also discussed in this paper. Experimental results demonstrate a better performance of the proposed methods in both perceptual quality and image quality assessment metrics than the existing histogram based methods.