This Python package implements four different algorithms for fusing a series of out of focus images into one sharp image. The following algorithms are supported:
| Algorithm | Published In |
|---|---|
| guided_filter | [1] |
| sparse_repr | [2] |
| dct_spatial_freq | [3] |
| linear_filters | [4] |
Simply run pip install . to install the package, then images can be fused via:
from focusfusion import focusfuse
from skimage.io import imread
img1 = imread('img1.png')
img2 = imread('img2.png')
img_fused = focusfuse([img1, img2], algorithm='sparse_repr')
Where sparse_repr can be replaced by any of the other available algorithms.
See help(focusfusion) for a more detailed description and possible fine
tuning options. The Jupyter notebook under demo contains some visual examples.
[1] S. Li, X. Kang and J. Hu, Image Fusion With Guided Filtering, in IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2864-2875, July 2013.
[2] B. Yang and S. Li, Multifocus Image Fusion and Restoration With Sparse Representation, in IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 4, pp. 884-892, April 2010.
[3] L. Cao, L. Jin, H. Tao, G. Li, Z. Zhuang and Y. Zhang., Multi-Focus Image Fusion Based on Spatial Frequency in Discrete Cosine Transform Domain, in IEEE Signal Processing Letters, vol. 22, no. 2, pp. 220-224, Feb. 2015.
[4] A. Kubota and K. Aizawa, Reconstructing arbitrarily focused images from two differently focused images using linear filters, in IEEE Transactions on Image Processing, vol. 14, no. 11, pp. 1848-1859, Nov. 2005.