Pattern Selective Image Fusion for Multi-focus Image Reconstruction

2005 ◽  
pp. 677-684 ◽  
Author(s):  
Vivek Maik ◽  
Jeongho Shin ◽  
Joonki Paik
Keyword(s):  
Image Reconstruction ◽  
Image Fusion ◽  
Focus Image

Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain

2016 ◽  
Vol 25 (10) ◽  
pp. 1650123 ◽  
Author(s):  
Sujoy Paul ◽  
Ioana S. Sevcenco ◽  
Panajotis Agathoklis
Keyword(s):  
Image Reconstruction ◽  
Image Fusion ◽  
Reconstruction Algorithm ◽  
Haar Wavelet ◽  
Reconstruction Technique ◽  
Fusion Algorithm ◽  
Poisson Solver ◽  
Pixel Location ◽  
Focus Image ◽  
Image Reconstruction Technique

A multi-exposure and multi-focus image fusion algorithm is proposed. The algorithm is developed for color images and is based on blending the gradients of the luminance components of the input images using the maximum gradient magnitude at each pixel location and then obtaining the fused luminance using a Haar wavelet-based image reconstruction technique. This image reconstruction algorithm is of [Formula: see text] complexity and includes a Poisson solver at each resolution to eliminate artifacts that may appear due to the nonconservative nature of the resulting gradient. The fused chrominance, on the other hand, is obtained as a weighted mean of the chrominance channels. The particular case of grayscale images is treated as luminance fusion. Experimental results and comparison with other fusion techniques indicate that the proposed algorithm is fast and produces similar or better results than existing techniques for both multi-exposure as well as multi-focus images.

Multi-focus Image Fusion Based on Multi-scale Gradients and Image Matting

2021 ◽  
pp. 1-1
Author(s):  
Jun Chen ◽  
Xuejiao Li ◽  
Linbo Luo ◽  
Jiayi Ma
Keyword(s):  
Image Fusion ◽  
Image Matting ◽  
Multi Scale ◽  
Focus Image

Gradient-based multi-focus image fusion method using convolution neural network

2021 ◽  
Vol 92 ◽  
pp. 107174
Author(s):  
Yang Zhou ◽  
Xiaomin Yang ◽  
Rongzhu Zhang ◽  
Kai Liu ◽  
Marco Anisetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Convolution Neural Network ◽  
Fusion Method ◽  
Gradient Based ◽  
Focus Image ◽  
Image Fusion Method

Enhancing vascular visualization in laser speckle contrast imaging of blood flow using multi-focus image fusion

2018 ◽  
Vol 12 (1) ◽  
pp. e201800100 ◽  
Author(s):  
Wenzhi Lv ◽  
Yang Wang ◽  
Xiao Chen ◽  
Xiaoxi Fu ◽  
Jinling Lu ◽  
...  
Keyword(s):  
Blood Flow ◽  
Image Fusion ◽  
Laser Speckle ◽  
Contrast Imaging ◽  
Laser Speckle Contrast Imaging ◽  
Speckle Contrast ◽  
Focus Image

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

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