The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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3.2 Inpainting Experiments 
(a) 5-pixel dead line (b) inpainted image of (a) 
(c) 8-pixel dead line (d) inpainted image of (c) 
Figure 5. Inpainting experimental results of CBERS images for 
the recovery of vertical dead lines. 
Figure 5 shows the inpainting experiments of CBERS (China- 
Brazil Earth Resource Satellite) images for the recovery of 
vertical dead lines. 
Figure 5(a) and 
Figure 5(c) are contaminated 
(a) scratched image (b) inpainted image of (a) 
(c) 60% dead pixels (d) inpainted image of (c) 
Figure 6. Inpainting experimental results of IKONOS images. 
by dead lines of 5-pixel width and 8-pixel width respectively. It 
is known that the conventional methods are not employable for 
such wide dead lines. 
Figure 5(b) and 
Figure 5(d) are the corresponding inpainted results using the 
proposed algorithm. Although the lost information cannot be 
completely recovered, the visual quality of the resulted images 
is very convincing. 
Figure 6(a) is a simulated image contaminated by some 
scratches, and 
Figure 6(b) shows the inpainted result. It is seen that most of 
the lost information has been recovered. 
Figure 6(c) assumes the image is contaminated by randomly 
distributed dead pixels whose percentage is 60%. The inpainted 
result is shown in 
Figure 6(d). This experiment validates the strong performance 
of the proposed algorithm. Although such random distribution 
of dead pixels is not very familiar to many remote sensing 
users, it is often met in remote sensing pre-processing before 
data distribution. 
4. CONCLUSIONS 
In this paper, we present a maximum a posteriori (MAP) based 
algorithm for both destriping and inpainting problems. The 
main advantage of this algorithm is that it can constrain the 
solution space according to a priori constraint during the 
destriping and inpainting processes. In the destriping 
experiments, we tested the proposed algorithm on Terra and 
Aqua MODIS images. The quantitative analysis showed that 
the proposed algorithm provides more assurance of desired 
results than the conventional destriping methods. In the 
inpainting experiments, the recovery of vertical, scratched and 
random dead pixels are respectively tested. Experimental 
results validated that the contaminated images can be 
noticeably improved by implementing the proposed algorithm. 
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