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2. BACKGROUND 
It should be noticed that the image discussed here are all 
captured in visible spectra bands, not relating to multi-spectra 
or hyper-spectra. 
For remote sensing image, the problem discussed above is 
relative to radiometry correction closely all the while. The task 
of radiometry correction is to build the quantitative relation 
between the signal quanta of each unit of the sensor and the 
actual ground object radiometry quanta the pixel correspond to 
the unit, develop the correction coefficient of the sensor directly, 
and realize the radiometry correction on the acquired data. 
Based on the causes of radiometry distortion, the radiometry 
correction can be performed in three directions. They are 
atmosphere correction, sensor correction and the correction for 
the distortion formed by the solar altitude angle and terrain 
affection. In physical sense, build a model that describes main 
elements in the imaging process can accurately and effectively 
restore the radiometry characteristic of the ground undoubtedly. 
A lot of researches have been performed on this topic, which 
includes building ground radiometry field [Zhang, 1995], 
atmosphere radiometry correction based on image [Tian, 1998], 
radiometry correction based on terrain [Yan, 2000; Li, 1995], 
and so on. It’s obvious that the ideal atmosphere radiometry 
correction and reflectance inversion should only make use of 
image information, instead of measuring support data in fields, 
and is practicable for history data and desolate area. Hence, 
although restore the surface spectra characteristic in the scene 
accurately should build a precise and complete model for 
quantitative remote sensing, for a mass of image analysis and 
applications such as photo interpretation, 3-D reconstruction, 
and feature extraction, accomplish the task of radiometry 
correction conveniently and effectively is still significant. 
Additional, for aero-borne image and close range image, the 
image degradation may directly affect the applications such as 
visual inspection, image retrieve, texture mapping and mosaic 
etc.. Because the imaging scale is relative smaller than remote 
sensing image, this two kinds of images are affected less by 
atmosphere relatively, but more by the geometry structure of 
the imaging scene. Therefore, building an illumination model to 
take into account all factors in the imaging process completely 
is difficult. Additional, with the improvement of the resolution 
of the sensors, the differences on resolution between kinds of 
scales become not significant any more. So, building a model or 
application framework only based on the image information 
and is available in most images is significant. 
Therefore, in the view of data type and affection factor, 
processing the affection on imaging scene formed by 
illumination environment usually has two directions. The first is 
to build a model. The second is to restore based on image 
information. And the latter is the main problem we considered. 
With the development of image capturing device, displaying 
device and output device, the application request on image 
realistic reproduction is improved too. So, the definition of 
dodging in photogrammetry and remote sensing should be 
extended to include color restoration, which means the problem 
dodging processed includes uneven lightness, uneven color, 
obvious color cast, realistic reproduction and geometry 
degradation to obtain realistic image suitable to human vision 
Psychology feature and physical feature, instead of 
conventional uneven lightness. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part BS. Istanbul 2004 
3. DODGING BASED ON ILLUMINATION MODEL 
Strictly speaking, the approaches based on physical model can 
deal with not only the dodging problem. In areas such as 
computer vision, machine vision, computer graphics, etc., 
building an illumination model can simulate the illumination 
environment to re-rendition the scene, including dodging and 
obtaining the representations in various illumination 
environments. So far, literatures indicate that the building of 
illumination model can performed in three directions: 
conventional illumination model, complete plenoptic function 
without illumination model, and image based rendering. [Shen, 
2000] 
3.1 Conventional Illumination Model 
After obtained the description on illuminant, surface reflectance 
in the scene, transitive characteristic of sensors, and geometry 
relation of the three elements above, builds a physical model to 
modify the color of each pixel. That is the basic framework of 
conventional illumination model [Fournier, 1993; Shum, 2000; 
Kang, 1999]. Some literatures [Wu, 2001; Danaher, 2001] have 
brought such method to bear on the radiometry correction on 
remote sensing data and achieved excellent result. Because the 
illuminant is mostly the sun, and supposing the ground is 
Lambertian, the primary task is to computer the bi-directional 
reflectance distribution function (BRDF). In most applications, 
take use of conventional illumination model should sample 
some ground objects! spectra distribution. Because such 
function is a 5-D function (including wavelength), the sampling 
and computation are tedious. Although the literature [Shen, 
2000] indicates that obtain some BRDF sample of the surface 
without practical measurement in the field is possible, such as 
calculates pseudo-BRDF of the surfaces based on the sky light 
[Yu, 1998], calculate day light spectra from the illumination 
color temperature based on empirical data [Takagi, 1990], 
extracts. BRDF from the reflect nature of the surface 
[Jaroszhiewiez, 2003], etc., whether the reflectance data is 
measured or not, to restore the BRDF of surface in the scene is 
still a challenging problem. For the applications in 
photogrammetry and remote sensing, such model can be 
simplified because the illuminant is ideal and only one - the sun. 
In remote sensing and aero-photogrammetry, because the 
objects and terrain affection are various and complex, surfaces, 
usually is the ground, can be looked as Lambertian, while in 
close range photogrammetry, the surface is usually the building 
surface or other special objects, they can't be looked as 
Lambertian. Therefore, building a conventional illumination 
model is an effective method in remote sensing image 
processing with reflectance data measured in field. However, 
it’s difficult in processing image in remote sensing without 
measured data and in close range applications, which make it be 
researched in depth in future to obtain practical method. 
3.2 Plenoptic Function without Illumination Model 
Because to restore the whole scene is a very difficult business, 
some researchers attempt to build a model to avoid the complex 
computation in the model building through controlling the 
variables in the scene and sampling densely. Such attempts take 
full advantage of the continuity of the plenoptic function. If the 
variables in the scene are looked as variables in the plenoptic 
function, a description of plenoptic function in a higher 
dimension is obtained. Such method without illumination model 
is equal to restore the continuous expression of that high 
dimension plenoptic through dense discrete sampling to predict