DESTRIPING AND INPAINTING OF REMOTE SENSING IMAGES 
USING MAXIMUM A-POSTERIORI METHOD 
Huanfeng Shen 3, *, Tinghua Ai a , Pingxiang Li b 
3 School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China - shenhf@whu.edu.cn 
b The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing 
Commission I, WG 1/1 
KEY WORDS: Correction, Retrieval, Algorithms, Image, Radiometric 
ABSTRACT: 
In a large number of spacebome and airborne multi-detector spectrometer imagery, there commonly exist image stripes and random 
dead pixels. The techniques to recover the image from the contaminated one are called image destriping (for stripes) and image 
inpainting (for dead pixels). In order to constrain the solution space according to a priori knowledge, this paper presents a maximum 
a posteriori (MAP) based algorithm for both destriping and inpainting problems. In the MAP framework, the likelihood probability 
density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the 
prior PDF. A gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been 
tested on images of different sensors. Experimental results show that it performs quite well in terms of both quantitative 
measurements and visual evaluation. 
1. INTRODUCTION 
Remote sensing images often suffer from the common problems 
of stripe noises and linear or random dead pixels. These 
severely degrade the quality of the measured imagery, and will 
introduce a considerable level of noise when processing data 
without correction of them. The correction of image stripes is 
commonly called as image destriping. The recovery of the dead 
pixels sometimes goes by the name of dead pixel replacement. 
In this paper, however, we use another more attractive name, 
i.e., image inpainting, which has been widely used in the field 
of digital image processing(Bertalmio et al., 2000). 
At the highest level, destriping techniques can be divided into 
frequency domain or spatial domain algorithms. The simplest 
frequency domain algorithm is to process the image data with a 
low-pass filter using discrete Fourier transform (DFT). This 
method has the advantage of being usable on geo-rectified 
images, but it often does not remove all stripes and leads to 
significant blurring within the image. Chen et al. (Chen et al., 
2003) proposed a method to distinguish the striping-induced 
frequency components using the power spectrum, and then 
remove the stripes using a power finite-impulse response filter. 
Some researchers remove the stripes using wavelet analysis 
which takes advantage of the scaling and directional properties 
to detect and eliminate striping patterns (Chen et al., 2006; 
Torres and Infante, 2001). 
In the spatial domain, most destriping algorithms examine the 
distribution of digital numbers for each sensor, and adjusts this 
distribution to some reference distribution (Gadallah et al., 
2000). These methods are equalization(Algazi and G. E. Ford, 
1981), histogram matching (Horn and Woodham, 1979; 
Wegener, 1990), moment matching (Gadallah et al., 2000), and 
others. More recently, Rakwatin et al. (Rakwatin et al., 2007) 
combined histogram matching with facet filter for stripe noise 
reduction in MODIS data. These methods have a similarity 
assumption for the image data. 
For the inpainting problem, the nearest-neighbor, average or 
median value replacement methods are commonly employed 
(Ratliff et al., 2007). The main disadvantage of these methods is 
that they are employable only when the dead area is small (for 
example, the width of the dead line is only one or two pixels). 
Even for dead areas just a little larger, these methods will 
produce obvious artifacts. 
In this paper, we formulate the destriping and inpainting 
problems using Maximum A Posteriori (MAP) estimation. Our 
motivation is to constrain the solution space of the ill-posed 
problems according to a priori knowledge on the form of the 
solution using the MAP framework. To our best knowledge, 
this is the first time that remote sensing destriping or inpainting 
problem is formulated using probabilistic approach. 
2. THE PROPOSED ALGORITHM 
2.1 Image Observation Model 
Letting z xy and g x v , respectively denote the input radiance to 
be measured and the senor output of location (x,y) , the 
relationship between z xy and g^, can be related by a linear or 
nonlinear function. In this paper, we assume the degradation 
process can be linearly described as in (Gadallah et al., 2000; 
* Corresponding author. 
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