Research of Multi-focus Image Fusion Algorithm Based on Sparse Representation and Orthogonal Matching Pursuit - Advances in Image and Graphics Technologies - page 63

(f) (g) (h) (i)

Fig. 4. ( Continued )

Table 3. Objective Evaluation Indicator Data to Multi-focus Fusion Image of Group C

Algorithm MI AG_A AG_B AG_F CC_A CC_B DD_A DD_B Q AB/F
8*8DCT 7.923 4.026 2.753 4.323 0.998 0.974 0.935 2.780 0.753
8*8KSVD 7.792 4.026 2.753 4.302 0.998 0.974 0.880

2.712 0.753

5.96 4.026 2.753 3.646 0.994 0.971 2.496 3.616 0.713

FSD

Contrast

7.161 4.026 2.753 4.427 0.997 0.974 1.278 2.687 0.741

DWT

6.447 4.026 2.753 4.562 0.996 0.975 1.481 2.805 0.712

SIDWT

6.833 4.026 2.753 4.201 0.996 0.947 1.455 2.946 0.728

SF

7.814 4.026 2.753 4.259 0.998 0.974 0.957 2.643 0.747

(4) Subjective and objective evaluations of each image fusion algorithm to the multi-
focus image of group D
Original multi-focus image data and fusion results of group D are shown in figure
5, and objective evaluation indicator data are shown in table 4.

(a) (b) (c) (d) (e)

(f) (g) (h) (i)
Fig. 5. Experimental Results of Multi-focus Image Fusion of Group D. (a)Foreground Fuzzy
Image (b)Background Fuzzy Image (c)8*8 DCT (d)8*8 K-SVD (e)FSD Algorithm (f)Contrast
Algorithm (g)DWT Algorithm (h)SIDWT Algorithm (i)Spatial Frequency Algorithm