text: In this work, a fast Bilateral Solver for Quality Estimation Based multi-focus Image Fusion method (BS-QEBIF) is proposed. The all-in-focus image is generated by pixel-wise summing up the multi-focus source images with their focus-levels maps as weights. Since the visual quality of an image patch is highly correlated with its focus level, the focus-level maps are preliminarily obtained based on visual quality scores, as pre-estimations. However, the pre-estimations are not ideal. Thus the fast bilateral solver is then adopted to smooth the pre-estimations, and edges in the multi-focus source images can be preserved simultaneously. The edge-preserving smoothed results are utilized as final focus-level maps. Moreover, this work provides a confidence-map solution for the unstable fusion in the focus-level-changed boundary regions. Experiments were conducted on @math pairs of source images. The proposed BS-QEBIF outperforms the other @math fusion methods objectively and subjectively. The all-in-focus image produced by the proposed method can well maintain the details in the multi-focus source images and does not suffer from any residual errors. Experimental results show that BS-QEBIF can handle the focus-level-changed boundary regions without any blocking, ringing and blurring artifacts.
  @cite_31: The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations.
  @cite_26: The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.
  @cite_56: A novel multi-focus image fusion approach using coupled dictionary training is proposed. It exploits the facts that (i) the patches in example data can be sparsely represented by a couple of over-complete dictionaries related to the focused and blurred categories of images and (ii) merging such representations is better than just selecting the sparsest one in the estimate of the original image. Inspired by these observations, we enforce the similarity of sparse representations between the focused and blurred image patches by jointly training the coupled dictionary, and then fuse these representations to generate an all-in-focus image by a fusion rule. The key characteristics of our approach are bridging the gap between coupled dictionaries, combining plain averaging and "choose-max" as an appropriate fusion rule, and forming a more accurate representation, compared to existing approaches which simply admit sparse representation over one dictionary. Extensive experimental comparisons with state-of-the-art multi-focus image fusion algorithms validate the effectiveness of the proposed approach.
  @cite_27: For most image fusion algorithms split relationship among pixels and treat them more or less independently, this paper proposes a region-based image fusion scheme using pulse-coupled neural network (PCNN), which combines aspects of feature and pixel-level fusion. The basic idea is to segment all different input images by PCNN and to use this segmentation to guide the fusion process. In order to determine PCNN parameters adaptively, this paper brings forward an adaptive segmentation algorithm based on a modified PCNN with the multi-thresholds determined by a novel water region area method. Experimental results demonstrate that the proposed fusion scheme has extensive application scope and it outperforms the multi-scale decomposition based fusion approaches, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.
  @cite_50: Supported by Navigation Science Foundation of P. R. China (05F07001) and National Natural Science Foundation of P. R. China (60472081)
  @cite_47: Optical lenses, particularly those with long focal lengths, suffer from the problem of limited depth of field. Consequently, it is often difficult to obtain good focus for all objects in the picture. One possible solution is to take several pictures with different focus points, and then combine them together to form a single image. This paper describes an application of artificial neural networks to this pixel level multifocus image fusion problem based on the use of image blocks. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach, particularly when there is a movement in the objects or misregistration of the source images.
  @cite_25: This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further.
summary: In the last decades, neural network and learning based methods were widely employed in image processing tasks and achieved impressive success. Some image fusion methods also try to utilize machine learning to help focus level estimation. For example, some methods @cite_25 @cite_27 @cite_26 @cite_50 adopt pulse coupled neural networks (PCNN) to do image fusion where PCNN does not require the training procedure. Some other methods @cite_47 @cite_56 @cite_31 train neural networks or dictionaries where focus level estimation was regarded as a classification problem to divide a local region into an in-focus or out-of-focus one. To do the training, @cite_56 @cite_31 collect some all-in-focus images and manually blur them using the Gaussian filters, while @cite_25 manually labels in-focus out-of-focus regions from source images as training sets.