# Exposure Bracketing Techniques for Camera Document Image Enhancement
## 3 HDR Document Image Generation
### Document Image Registration
Area-based methods, however, are not employed due to two reasons: (1) the first reason is that this method is computationally heavy as it performs cross correlation on multiple image patches and (2) the second reason is that image patches under different exposure levels may display extremely different characteristics, which may fail cross the correlation method. The second method to estimate the planar homograph transformation is called the feature-based method. Two critical steps in feature-based methods are feature extraction and feature matching. We expect that the extracted features will be consistent regardless of exposure levels and among all the feature extraction methods, we selected the Scale-invariant Feature Transform (SIFT) method [8] because it improves detection stability in situations of illumination changes. In the meantime, it achieves almost real-time performance and the features that are detected are highly distinctive. SIFT does not only define the position of detected points, but also provides a description of the region around the feature point by means of a descriptor, which is then used to match SIFT feature points. Therefore, we have used the SIFT method to find CP pairs. Figure 5 shows the extracted matched SIFT features for two LDR images. As we can observe from Figure 5, some SIFT feature points that cannot find correct corresponding pairs always exist. Therefore, some CP pairs cannot be used for inferring the planar homograph model as they are outliers. In order to remove these outliers, we used the Random Sample Consensus (RANSAC) algorithm combined with spatial constrains to prone the outliers [9]. The basic idea behind this is that we can estimate the planar homograph model with four randomly selected points. With the estimated model, we can check how close the CP pair is if they are put in the same coordinate system after transformation with formula (1). Then we calculate how many CP pairs are consistent with the estimated projective transform model, which indicates the confidence level of the estimated projective transform model. We performed this procedure multiple times and selected the projective model with the highest confidence level. After that, we re-estimated the projective model once again using the least-square method with all the CPs that fit the selected model. Figure 6 shows the selected CPs that can be used to infer the planar homograph model using the above point pruning procedure. From this figure, we can clearly observe that all the outliers have been removed and each SIFT point in one image can always find its correct counterpart in another image. After the planar homograph model is estimated, we can perform the registration so that pixels from the same target share the same coordinate in the image stack. Figure 7 shows the pseudo color image after the registration, where the color configuration is the same as Figure 4. Overlapping the registered image of different exposure levels shows that a decent registration result has been obtained. Figure 5: (**a**) Matched Scale-invariant Feature Transform (SIFT) feature points for a well-exposed image (Figure 1a); (**b**) Extracted SIFT feature points for an over-exposed image (Figure 1c). Figure 6: (**a**) Selected SIFT feature points for a well-exposed image (Figure 1a); (**b**) Selected SIFT feature points for an over-exposed image (Figure 1c).