基于超像素级卷积神经网络的多聚焦图像融合算法

该文提出了基于超像素级卷积神经网络(sp-CNN)的多聚焦图像融合算法。该方法首先对源图像进行多尺度超像素分割,将获取的超像素输入sp-CNN,并对输出的初始分类映射图进行连通域操作得到初始决策图;然后根据多幅初始决策图的异同获得不确定区域,并利用空间频率对其再分类,得到阶段决策图;最后利用形态学对阶段决策图进行后处理,并根据所得的最终决策图融合图像。该文算法直接利用超像素分割块进行图像融合,其相较以往利用重叠块的融合算法可达到降低时间复杂度的目的,同时可获得较好的融合效果。     

基于双子代差分演化和自适应分块机制的多聚焦图像融合算法

基于分块的多聚焦图像融合算法是多聚焦图像融合领域中的一个重要算法。基于差分演化的多聚焦图像融合算法将图像分块大小作为差分演化算法的种群,通过多次演化,最后获得使融合图像效果最好的图像分块。为克服标准差分演化算法由于丢失父代种群的部分信息导致收敛速度变慢、全局搜索范围较小,以及当对应图像块的清晰度相等时该算法的处理方式会改变源图像的像素值的缺点,在原算法的基础上,引入双子代机制和自适应分块机制,提出一种基于双子代差分演化和自适应分块机制的多聚焦图像融合算法。在演化过程中生成两个子代种群,最大程度上保留父代种群的信息,扩大全局搜索范围,提高算法的收敛性能;利用自适应分块机制,当出现图像块清晰度相等的情况时,将图像块分解成更小的图像块,然后再进行清晰度的比较,使改进算法获得的融合图像比原算法获得的效果更好,而且不会改变源图像的像素值。实验结果表明,基于双子代差分演化和自适应分块机制的多聚焦图像融合算法可以获得比原算法效果更好的融合图像,而且收敛性能更好。     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

Multi-scale weighted gradient-based fusion for multi-focus images

Anisotropic blur and mis-registration frequently happen in multi-focus images due to object or camera motion. These factors severely degrade the fusion quality of multi-focus images. In this paper, we present a novel multi-scale weighted gradient-based fusion method to solve this problem. This method is based on a multi-scale structure-based focus measure that reflects the sharpness of edge and corner structures at multiple scales. This focus measure is derived based on an image structure saliency and introduced to determine the gradient weights in the proposed gradient-based fusion method for multi-focus images with a novel multi-scale approach. In particular, we focus on a two-scale scheme, i.e., a large scale and a small scale, to effectively solve the fusion problems raised by anisotropic blur and mis-registration. The large-scale structure-based focus measure is used first to attenuate the impacts of anisotropic blur and mis-registration on the focused region detection, and then the gradient weights near the boundaries of the focused regions are carefully determined by applying the small-scale focus measure. Experimental results clearly demonstrate that the proposed method outperforms the conventional fusion methods in the presence of anisotropic blur and mis-registration.     

一种多聚焦图像融合方法

提出一种基于小波变换的多聚焦图像融合方法．该方法首先通过小波变换得到源图像的小波系数，然后对低频和高频分量采用不同的融合规则和融合算子构造融合图像的小波系数，最后基于一致性检测由高低频融合系数得到最终的融合结果．用两组源图像数据验证所提出的融合算法，并与其他融合算法进行比较，仿真结果表明该算法在多聚焦图像融合中优于其他方法．     

多聚焦图像灰度重叠特征自适应识别仿真

传统方法对多聚焦图像进行预处理,由于图像灰度重叠区域合并使原图像细节信息损失,导致多聚焦图像灰度重叠区域识别效果不理想,为此提出基于Mean-shift算法和OTSU阈值分割算法的多聚焦图像灰度重叠特征自适应识别方法。使用Mean-shift算法对多聚焦图像进行平滑处理,对平滑处理过后的多聚焦图像进行小波变换,将图像的灰度重叠区域灰度值增强;再使用阈值分割将经过灰度增强的重叠区域分类;通过OTSU算法识别出灰度重叠特征区域。实验结果表明,提出方法在图像灰度重叠区域的识别效果上较为突出,并且能够有效保留灰度重叠区域的细节信息。     

一种新的像素级多聚焦图像融合算法

该文在小波变换的基础上提出了一种将一维自组织特征映射(SOFM)网络和进化策略相结合的多聚焦图像融合算法。该方法对不同聚焦点的图像进行冗余小波分解,再分别将其各方向、各尺度的高频信息进行叠加,并在高频信息叠加层上提取反映图像清晰度差异的归一化特征图,依据此特征图,使用SOFM网络对原始图像像素进行分类,并利用进化策略对各类像素求出最优的融合系数。实验结果表明该算法比拉普拉斯变换法和小波变换法具有更好的融合效果。     

Texture analysis-based multi-focus image fusion using a modified Pulse-Coupled Neural Network (PCNN)

Multi-focus image fusion is an effective method of information fusion that can take a series of source images and obtain a fused image where everything is in focus. In this paper, a multi-focus image fusion method based on image texture that adopts a modified Pulse-Coupled Neural Network (PCNN) approach is proposed. First, the texture of an image is obtained by means of image cartoon and texture decomposition. An ignition image is then acquired by inputting the image textures into a modified PCNN. Ignition images are compared to each other to obtain an initial decision map. A small object detection and bilateral filter is then applied to the initial decision map to reduce noise and enable smoother processing. Finally, the source images and decision map are used to produce the fused image. Experimental results demonstrate that the proposed method effectively preserves the source images information while delivering good image fusion performance.