快速离散Curvelet变换域的图像融合

目的单一图像往往难以捕获一个场景下所有的细节信息,针对这一问题,可以通过多传感器或同一传感器的不同方式来获取多幅图像,然后通过图像融合技术将获得的多幅图像进行融合。为了提高图像融合的质量,提出一种基于快速离散Curvelet变换(FDCT)的图像融合新方法。方法不同于以往的方法,提出一组新的融合规则。分别采用基于局部能量和改进拉普拉斯能量和的方法,通过对FDCT分解得到的低频和高频系数进行系数选择,然后对得到的融合系数进行FDCT逆变换重构得到融合图像。结果通过对大量的多模态医学图像、红外可见光图像以及多聚焦图像进行图像融合实验,无论是运用视觉的主观评价,还是均值、标准差、信息熵以及边缘信息保持度等客观评价标准,本文方法都优于传统的基于像素平均、小波变换、FDCT以及双边梯度等融合方法。结论对比现有的方法,本文方法对多模态和多聚焦等形式的图像融合都表现出优越的融合性能。     

Optimizing image focus for 3D shape recovery through genetic algorithm

Three-dimensional information of objects is advantageous and widely used in multimedia systems and applications. Shape form focus (SFF) is a passive optical technique that reconstructs 3D shape of an object using a sequence of images with varying focus settings. In this paper, we propose an optimization of the focus measure. First, Wiener filter is applied for noise reduction from the image sequence. At the second stage, genetic algorithm (GA) is applied for focus measure optimization. GA finds the maximum focus measurement under a fitness criterion. Finally, 3D shape of the object is determined by maximizing focus measure along the optical direction. The proposed method is tested with image sequences of simulated and real objects. The performance of the proposed technique is analyzed through statistical criteria such as root mean square error (RMSE) and correlation. Comparative analysis shows the effectiveness of the proposed method.     

Shape from focus

The shape from focus method presented here uses different focus levels to obtain a sequence of object images. The sum-modified-Laplacian (SML) operator is developed to provide local measures of the quality of image focus. The operator is applied to the image sequence to determine a set of focus measures at each image point. A depth estimation algorithm interpolates a small number of focus measure values to obtain accurate depth estimates. A fully automated shape from focus system has been implemented using an optical microscope and tested on a variety of industrial samples. Experimental results are presented that demonstrate the accuracy and robustness of the proposed method. These results suggest shape from focus to be an effective approach for a variety of challenging visual inspection tasks     

Multifocus image fusion using focus measure of fractional differential and NSCT

In this paper, a new multifocus image fusion scheme based on fractional differential and NSCT is proposed. Firstly, in virtue of the properties of fractional differential, a novel focus measure in nonsubsampled contourlet transform (NSCT) domain is presented and used to determine which coefficient is from the focused region. Then, based on the imaging principle of the multifocus image and the focus measure, a new selection principle for the lowpass subbands coefficients is developed. Meanwhile, focus measure maximum choosing scheme, namely select the coefficient with maximum focus measure value as the corresponding coefficient of the fused image, is applied to the high-frequency subbands. Finally, the inverse NSCT is employed to reconstruct the fused image and a pleasing fused result is generated. The experimental results show that the proposed method outperforms the conventional multifocus image fusion methods in both subjective and objective qualities.     

Accurate recovery of three-dimensional shape from image focus

A new shape-from-focus method is described which is based on a new concept, named focused image surface (FIS). FIS of an object is defined as the surface formed by the set of points at which the object points are focused by a camera lens. According to paraxial-geometric optics, there is a one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of shape recovery can be posed as the problem of determining the shape of the FIS. From the shape of FIS the shape of the object is easily obtained. In this paper the shape of the FIS is determined by searching for a shape which maximizes a focus measure. In contrast to previous literature where the focus measure is computed over the planar image detector of the camera, here the focus measure is computed over the FIS. This results in more accurate shape recovery than the traditional methods. Also, using FIS, a more accurate focused image can be reconstructed from a sequence of images than is possible with traditional methods. The new method has been implemented on an actual camera system, and the results of shape recovery and focused image reconstruction are presented     

Optimal depth estimation by combining focus measures using genetic programming

Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the heterogeneous function is more effective than homogeneous function.     

A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion

Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.     

各向异性扩散与高斯差分模型的多聚焦图像融合方法

利用各向异性扩散模型具有良好的边缘保持特性,提出一种基于各向异性扩散滤波与高斯滤波差分规则的图像融合算法。各向异性扩散方程对图像进行滤波操作,在图像的同质区域实施正向扩散以平滑图像,而在图像边缘实行较弱平滑以保护边缘细节信息。将通过各向异性扩散模型处理的图像与经过高斯函数滤波的结果图像进行差分操作,可以得到图像的高频系数信息。为提高健壮性,对高频系数进行小窗口累加,其作为像素选择准则,再分别从原始图像中直接获取对应的像素值组成融合结果图像。实验结果表明,所提出的方法可以有效地融合源图像信息,非常适合多聚焦     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

A novel iterative shape from focus algorithm based on combinatorial optimization

Shape from focus (SFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. In this paper, the SFF is presented as a combinatorial optimization problem. The proposed algorithm tries to find the combination of pixel frames which produces maximum focus measure computed over pixels lying on those frames. To reduce the high computational complexity, a local search method is proposed. After the estimate of the initial depth map solution of an object, the neighborhood is defined, and an intermediate image volume is generated from the neighborhood. The updated depth map solution is found from the intermediate image volume. This update process of the depth map solution continues until the amount of improvement is negligible. The results of the proposed SFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing SFF methods.