# Exposure Bracketing Techniques for Camera Document Image Enhancement
## 1 Introduction  
Camera document images provide many possibilities for further processing (for example, Optical Character Recognition (OCR)) if the captured image quality is good [1]. However, the captured image quality may vary depending on the imaging conditions. One difficult case where the captured image is far from ideal is when there is uneven lighting. In this case, some regions in the captured image are over-exposed while other regions are under-exposed. Though adaptive imaging processing techniques such as the Sauvola binarization method have been proposed to solve this problem [2], when the uneven lighting is severe, the lost textual information due to over-exposure or under-exposure cannot be retrievable as this information is not captured by the camera's sensor.  
In this paper, we considered using an exposure bracketing technique to solve the uneven lighting problem. Exposure bracketing is a technique of formulating one single image by combining multiple images of the same scene but with different exposure times [3]. In the uneven lighting environment, irradiance across the scene varies greatly, leading to over-exposed imaging areas and under-exposed imaging areas regardless of the set exposure time. These over-exposed and under-exposed areas carry less information than the same areas when they are well-exposed. In order to capture details about an entire scene, it is necessary to capture images at multiple exposures. In doing so, the over-exposed or under-exposed image patches in one image can always find their corresponding patches in another image where the imaging area was well-exposed.  
Generating images with different exposure times is becoming much easier nowadays because more and more consumer cameras or smart phones allow the user to manually adjust the exposure time when capturing. Document images that are captured with different exposure times show different characteristics. Figure 1 shows such an example where three exposure images are generated for the same document. It is obvious that these images contain complementary information. For example, the left-bottom part in Figure 1a is under-exposed, but that area is well-exposed in Figure 1c; the left central part in Figure 1a is well-exposed, while that area is over-exposed in Figure 1c. These three images are captured with the Huawei Phone P20 in the same lighting condition and for the under-exposed case, the exposure time is 1/800 s; for the well-exposed case, the exposure time is 1/320 s; and for the over-exposed case, the exposure time is 1/40 s. Intuition tells us that we can generate a better image by selecting the best image patches or pixels from these three images.  
In the following sections, first of all we will have a technical overview of exposure bracketing techniques. After that, we will disclose a registration method that particularly suits document image exposure bracketing. Then, we will select four representative exposure bracketing algorithms and use them in the context of document image exposure bracketing. In the experiment section, we will compare these methods and see how different exposure bracketing methods help to increase OCR accuracy and finally, we will draw our conclusion as well as directions for future research.