The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
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proposed algorithm, every object in an image is encapsulated by 
a tree structure, where root of the object tree is the brightest 
pixel in the object and leaves are the pixels at 
object/background boundaries. The pixels belonging to the 
object but lying between the root and the leaves are non 
terminal nodes of the object tree. Many object trees are thus 
created all over the image corresponding to various objects. All 
object trees whose root gray value is greater than or equal to 
1000 are identified as candidates for smoothing. A mask 
identifying pixels corresponding to such object trees 
(comprising of root, nodes and leaves) is generated. Smoothing 
(3x3 averaging) is done only for the masked pixels in the image. 
Such a selectively smoothed image forms the input for MTF 
correction. Due to prior smoothing of bright objects, ringing 
artifacts do not arise in the restored image. 
4.1.3 Artifact Removal Post-processing: 
Post-processing involves spatial neighborhood operation on the 
restored imagery to remove false shadows/dark spots. Original 
un-restored image and restored image form input to the 
proposed algorithm. All pixels in the restored image, where 
ratio of restored and unrestored values exceeds a threshold (say 
2.0) are firstly identified. The intensity of such pixels is 
recomputed using corresponding un-restored gray value from 
original image and restored gray value from the restored image. 
4.1.4 Wavelet based denoising: 
The imaging mechanism (in this case, pushbroom scanning by 
an imaging sensor mounted on a satellite) acts as a low-pass 
filter and introduces degradation and noise in the observed 
image. The degradation is less for the lower spatial frequencies 
as compared to the mid and high spatial frequencies. As the 
high spatial frequencies (eg. features like edges) suffer more 
degradation (appear blurred), the signal power at high 
frequencies is low. However, the noise power is uniform at all 
frequencies (such type of noise is termed as ‘white’ noise). 
Therefore, at high frequencies, noise dominates over the signal. 
Restoration filter achieves deblurring by amplifying the high 
frequencies. The gain of the restoration filter is adjusted such 
that it is high at mid and high frequencies and unity at low 
frequencies. As a result, after restoration, the image appears to 
be deblurred (sharp), but noisy. The nature of noise that was 
‘white’ prior to restoration is also changed to ‘colored’ during 
the process due to unequal gains of restoration filter. The 
amplified noise is particularly noticed in the uniform areas of 
the image. 
The stagger removed, MTF corrected (restored) and artifact 
removed image is decomposed into complex sub-bands using 
the dual tree complex wavelet transform. The signal is assumed 
to be concentrated on the larger scale coefficients, while noise is 
distributed with same variance over all the coefficients. As the 
signal and noise are located in nearly separate sub bands, the 
next step is to efficiently estimate the threshold that can 
eliminate the noisy coefficients. The thresholds are calculated 
using the adaptive ‘shrink’ method. The new wavelet 
coefficients are calculated by applying soft thresholding on the 
sub bands. Inverse transform of the thresholded bands gives the 
denoised image. The complex dual tree discrete wavelet 
transform is very efficient in higher noise conditions. 
4.2 Restoration of Cartosat-1 Imagery: 
The laboratory measured point spread functions for the Fore and 
Aft sensors of Cartosat-1 were taken as the degradation function 
for restoration of respective imagery. Wiener filter, which 
incorporates the degradation function as well as the model of 
noise, was designed in frequency domain for each sensor. After 
several experiments and independent qualitative and 
quantitative evaluations, the noise to signal ratio (nsr) was 
modeled to vary exponentially between nsrmin and nsrmax for 
low to high frequencies. The values of nsrmin and nsrmax were 
tuned suitably to control sharpness and noise. 
Restoration of the fore and aft images was performed block- 
wise. Every block of the input image was filtered with the 
Wiener filter in the frequency domain. Overlap of few pixels 
was maintained between successive blocks to avoid artifacts at 
block boundaries. Fast Fourier Transform (FFT) techniques 
were used to achieve high speed. The results of restoration are 
shown in Figures 3-6. Subsequent to restoration, Wavelet based 
denoising was performed. 
4.3 Evaluation: 
Apart from visual improvements (see Figures 3-6), the restored 
and denoised Cartosat-1 stereo images were independently 
evaluated quantitatively for their performance improvement in 
DEM generation. DEMs were generated from original stereo 
orthokit products as well as MTF enhanced products and the 
resulting DEMs were compared with high accuracy reference 
DEMs provided by respective Principal Investigators for two 
test sites, namely Hobart in Australia and Castel Gandolfo near 
Rome in Italy. The results are shown in Tables 1 and 2 and 
Figures 1 and 2. It could be observed in Tables 1 and 2 the third 
column values are consistently found larger than the 
corresponding values in column 2 indicating the enhanced 
performance in DEM generation due to the MTF improvements 
carried out on the stereo images. DEM improvements have 
expectedly been resulted due to more accurate stereo image 
matching after MTF enhancements. To observe the visual 
quality improvements in Figures 3 to 6, readers are 
recommended to observe them in softcopy version of this Paper 
accessible from ISPRS web site. 
Height 
Difference 
Comparison of DEM 
generated from 
Original Orthokit 
with Reference DEM 
Cumulative 
Percentage 
Comparison of DEM 
generated from MTF 
Enhanced Orthokit 
with Reference DEM 
Cumulative 
Percentage 
Up to 1 m 
37.5 
43.25 
Up to 3 m 
55.16 
59.98 
Up to 5 m 
70.03 
70.74 
Up to 10 m 
89.76 
91.65 
Table-1 : DEM Results for Hobart Test Site 
Height 
Difference 
Comparison of DEM 
generated from 
Original Orthokit 
with Reference DEM 
Cumulative 
Percentage 
Comparison of DEM 
generated from MTF 
Enhanced Orthokit 
with Reference DEM 
Cumulative 
Percentage 
Up to 1 m 
18.39 
18.58 
Up to 3 m 
58.67 
62.15 
Up to 5 m 
75.81 
78.88 
Up to 10 m 
84.97 
93.12 
Table-2: DEM Test Results for Castel Gandolfo Test Site