Multi-scale Analysis based Image Fusion

Image fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa

Multi-scale Analysis based Image Fusion

The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, JapanImage fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper

Image Fusion Algorithm Based on Spatial Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

Nonsubsampled contourlet transform (NSCT) provides °exible multiresolution, anisotropy and directional expansion for images. Compared with the original contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. Pulse Coupled Neural Networks (PCNN) is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion. In this paper, NSCT is associated with PCNN and employed in image fusion to make full use of the characteristics of them. Spatial frequency in NSCT domain is input to motivate PCNN and coe±cients in NSCT domain with large firing times are selected as coe±cients of the fused image. Experimental results demonstrate that the proposed algorithm outperforms typical wavelet-based, contourlet-based, PCNN-based and contourlet-PCNN-based fusion algorithms in term of objective criteria and visual appearance.Supported by Navigation Science Foundation of P. R. China (05F07001) and National Natural Science Foundation of P. R. China (60472081

Research on Multi-focus Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

图像融合是一门综合了传感器、图像处理、信号处理、计算机和人工智能等多种学科的现代高新技术。图像融合的主要思想是采用一定的算法，把来自多个传感器的多源图像综合成一幅新的图像，新的融合图像更符合人眼视觉习惯和机器感知。图像融合不是简单的叠加，它产生新的蕴含更多有价值信息的图像。目前，图像融合技术已广泛应用于遥感、军事、机器人以及医学处理等领域。 本文的研究重点是基于无下采样Contourlet变换的多聚焦图像融合算法。主要工作内容如下： 1、提出了一种基于改进区域方差的多聚焦灰度图像融合算法。对多聚焦灰度图像经无下采样Contourlet变换后分解得到的低频部分采用改进的加权平均融合规则，对分...Image fusion is a comprehensive modern high technology including many subjects, such as sensor technology, image processing, signal processing, computer, artificial intelligence, etc. We use the term of image fusion to denote a process. Such process generates a single image which contains a more accurate description of the scene than any of the individual source images. This fused image should be ...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302007115132

An Image Fusion Algorithm Based on Contourlet Transform

图像融合是一项综合同一场景多源图像信息，得到一幅同一场景图像的技术，在图像理解和计算机视觉领域中有着重要的应用价值。从军事应用为目的的数据融合技术开始，融合技术已广泛用于资源管理、城市规划、气象预报、作物及地质分析等领域。本文从变换方法和融合算法两个方面综合研究了多源图像融合技术，提出了一种基于Contourlet变换的改进PCNN融合算法。该算法从变换域和融合算法两个方面对融合进行改进，通过对比多层PCNN神经元的点火次数，更好地提取源图像特征系数，有效保留图像的纹理细节，大大改善了融合结果。 首先介绍了基于小波分解的图像融合算法，给出了小波分解图像融合的实现方案，并对影响该算法的融合结果...Image fusion，which is an important and useful technique for image analysis and computer vision in recent years, is a technique to combine multiple images of the same scene into a new one. This technique has been widely used not only in military application, but also in industry and agriculture fields, such as resources management，town planning，weather forecast and geological analysis. With studyin...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2005130240

Image Haze Removal Algorithm Based on Nonsubsampled Contourlet Transform

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

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations