Generally, an acquired stereoscopic image pair needs to be pre-processed geometrically before 3D viewing, because the
parallax and alignment of the pair is not optimal for binocular vision. A stereo image obtained without using a
specialized stereo camera system can have several problems that disrupt comfortable 3D viewing, such as insufficient or
excessive baseline lengths between the two images. We present a reconstruction technique for the stereo pair images that
maximizes visual comfort. First, a disparity map is generated from the stereo image by multiple footprints stereo
algorithm, and then a synthetic stereomate is created using the disparity map and a right image of the given stereo pair.
At this time, we adjust the disparity map to create a more realistic 3D effect. Most of the frequency disparity is
reassigned to zero and the maximum disparity is revised as a parallax comfortable for human eyes. The occlusion of the
synthetic stereomate is corrected by an inpainting method. Through the experiments, we could obtain a registered
stereoscopic image with an optimized parallax. To evaluate the proposed technique, our results were compared with the
original stereo pairs by viewing the 3D stereo anaglyphs.
Preservation of spectral information and the enhancement of spatial resolution are regarded as very important in satellite
image fusion. In previous research, many algorithms simultaneously unsolved these problems, or needed experimental
parameters to enhance fusion performance. This paper proposed a new fusion method based on fast intensity-huesaturation
(FIHS) to merge a high-resolution panchromatic image with a low-resolution multispectral image. It is conducted by multiple regressions for generating synthetic image and statistical ratio-based image enhancement, which is presented as solving the spectral distortion and conserving the spatial information of the panchromatic image. IKONOS datasets were employed in the evaluation. The results showed that the proposed method was better than the widely used image fusion methods, including the FIHS-based method and the Pan Sharpening module in PCI Geomatica. We compared widely used algorithms with adaptive FIHS image fusion using various fusion quality Indexes such as ERGAS, RASE, correlation, and the Q4 index. The images obtained from the proposed algorithm present higher spectral and spatial quality than the results from using other fusion methods. Therefore, the proposed algorithm is very efficient for high-resolution satellite image fusion with an automatic process.
Anaglyph is the simplest and the most economical method for 3D visualization. However, anaglyph has several drawbacks such as loss of color or visual discomfort, e.g., region merging and the ghosting effect. In particular, the ghosting effect, which is caused by green penetrating to the left eye, brings on a slight headache, dizziness and vertigo. Therefore, ghosting effects have to be reduced to improve the visual quality and make viewing of the anaglyph
comfortable. Since red lightness is increased by penetration by green, the lightness of the red band has to be compensated for. In this paper, a simple deghosting method is proposed using the red lightness difference of the left and right images. We detected a ghosting area with the criterion, which was calculated from the statistics of the difference image, and then the red lightness of the anaglyph was changed to be brighter or darker according to the degree of the difference. The amount of change of red lightness was determined empirically. These adjustments simultaneously reduced the ghosting effect and preserved the color lightness within the non-ghosting area. The proposed deghosting method works well, and the goal of this paper was to detect the ghosting area automatically and to reduce the ghosting.
The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and as the need for standard sensor model has become important, the applicability of the RPC model is also increasing. The RPC model can be substituted for all sensor models, such as the projective, the linear pushbroom and the SAR. This paper is aimed at generating a RPC model from the physical sensor model of the KOMPSAT-1 (Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects 510 ~ 710 nm panchromatic images with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. We generated the RPC from a physical sensor model of KOMPSAT-1 and aerial photography. The iterative least square solution based on Levenberg-Marquardt algorithm is used to estimate the RPC. In addition, data normalization and regularization are applied to improve the accuracy and minimize noise. And the accuracy of the test was evaluated based on the 2-D image coordinates. From this test, we were able to find that the RPC model is suitable for both KOMPSAT-1 and aerial photography.