Research On Pixel Level Fusion Algorithms For Multifocus Image

Multi-focus image fusion is an important branch of multi-sensor image fusion. It is mainly employed in the fusion of different target-specific focusing on the integration of multiple images. These images are attained by the same optical sensor at the same imaging condition. Since the limited depth-of-focus of optical lenses, it is difficult to get an image that contains all the relevant targets in focus. Moreover, it is time/space and energy consuming to analysis large number of similar images. Multi-focus image fusion can solve this problem efficiently. This technology can improve the utilization of image information, extend the work's scope of system and strengthen the reliability of system, and more precise and reliable representation of the scene can be attained. Currently, this technology is widely employed in verticals such as medical care, transportation, logistics and military.Pixel level fusion for multi-focus image is the base of image fusion, which can get more original information and provide more detail information. How to locate the focused region is the key to multi-focus image fusion and one of the difficulties. For the affection of image content, it is difficult to accurately locate and extract the focused regions in source images, which may compromise the quality of fused image. For the shortage of the existing methods, this paper studied the multi-focus image fusion methods in the spatial domain, respectively.A multi-focus image fusion method based on (robust principal component analysis) RPCA and Pulse Coupled Neural Network (PCNN) in RPCA decomposition domain is proposed. Since RPCA can represent the high dimension in a lower dimensional linear space and strengthen the target information, which is robust to noise. Thus, the sparse feature of the source images in RPCA decomposition domain is used as the external input of PCNN neuron and the sharp region are selected by the fire times of PCNN neuron, which suppresses the noise and improves the fused quality.A multi-focus image fusion algorithm based on RPCA with Quad tree (QT) decomposition is proposed. QT decomposition partitions the image based on the regional homogeneity of sparse matrix, which is useful to safeguard the integrity of regional information. QT decomposition saves the partition results with tree structure, which improves the efficiency of block division. Thus, this paper performs the QT decomposition on sparse matrix of source images in RPCA decomposition domain and determines the optimal block size based on the regional homogeneity of sparse matrix, and then, sharp regions of source images are selected based on the local feature of each sparse matrix block. Thus, the affection of blocking artifacts is suppressed and better fusion results are attained.A multi-components multi-focus image fusion algorithm based on image decomposition is proposed. Since multicomponent of image can provide more complete representation for an image, this algorithm decomposes the source images into cartoon and texture components by using Split Bregman algorithm of Rudin-Osher-Fatemi (ROF). The focused pixels are detected by the energy of image gradient (EOG) of the neighborhood of each pixel of the cartoon and texture components. The focused pixels are fused in different fusion rule, respectively. The final fused image can be attained by merging the cartoon and texture components. This algorithm makes up for the shortage of traditional fusion method and improves the performance of fusion method in representation integrity of image detail information.A multi-focus image fusion scheme based on NMF and fused regions detection is proposed. The scheme fuses the source images to construct initial fused image by using the pure additive and sparsity of the NMF. The local features of the difference between initial fused image and source images are used to detect the fused regions of source images. The focused regions are merged to construct the final fused image based on fusion rules. The scheme can efficiently improve the fusion quality and the visual effects.Finally, a summary of the research work and the contribution is presented. In addition, the future work and the target are pointed out.