![]() ![]() Guo D, Yan J, Qu X (2015) High-quality multi-focus image fusion using self-similarity and depth information. Garnico-Carrillo A, Calderon F, Flores J (2018) Multi-focus image fusion by local optimization over sliding windows. Information Fusion 45:96–112įu W, Huang S, Li Z, Shen H, Li J, Wang P (2016) The optimal algorithm for multi-source RS image fusion. ![]() ![]() Optik 176:567–578įarid MS, Mahmood A, Al-Maadeed SA (2019) Multi-focus image fusion using content adaptive blurring. Neurocomputing 215:3–20ĭu C, Gao S, Liu Y, Gao B (2019) Multi-focus image fusion using deep support value convolutional neural network. Optik 157:1003–1015ĭu J, Li W, Lu K, Xiao B (2016) An overview of multi-modal medical image fusion. SIViP 12:271–279Ĭhen C, Gend P, Lu K (2015) Multi-focusImage fusion based on multiwavelet and DFBĭu C, Gao S (2018) Multi-focus image fusion algorithm based on pulse coupled neural networks and modified decision map. Information Fusion 45:113–127Ĭhaudhary V, Kumar V (2018) Block-based image fusion using multi-scale analysis to enhance depth of field and dynamic rang. ![]() Computers, Computers and Electrical Engineering 65:139–152Īymaz S, Köse C (2019) A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion. Information Fusion 51:201–214Īnandhi D, Valli S (2018) An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled counterlet transform. Computers and Electrical Engineerring 51:74–88Īmin-Naji M, Aghagolzadeh A, Ezoji M (2019) Ensemble of CNN for multi-focus image fusion. The results show that the proposed method produces high quality images with clear edges and transmits most of the information of source images into all-in-focused image.Ībdipour M, Nooshyar M (2016) Multi-focusimage fusion using sharpness criteria for visual sensor networks in wavelet domain. Besides these features, the new dataset which is different from the datasets in the literature is created and used firstly in this paper. Lastly, the performance evaluation of proposed method is measured using three different metrics which are objective, subjective and time criterion metrics. The each pixel of fused sub-bands is created using these weight coefficients and fused image is reconstructed using Inverse Stationary Wavelet Transform. The weight coefficients which show the importance rates of corresponding pixels in source images for fused image are calculated using designed formula based on gradient magnitudes. Then, a new fusion rule which depend on gradient-based method with sobel operator is implemented to create fused images with good visuality. Secondly, source images with high resolution are decomposed into four sub-bands which are LL (low-low), LH (low-high), HL (high-low) and HH (high-high) using Stationary Wavelet Transform with dmey (Discrete Meyer) filter. Firstly, the information of source images is enhanced using bicubic interpolation-based super-resolution method. In this paper, a new approach for multi-focus image fusion is proposed. All-in-focused image has more information, clearer parts and clearer edges than the source images. Multi-focus image fusion methods combine two or more images which have blurred and defocused parts to create an all-in-focused image. ![]()
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