# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 1 Introduction
In this method, some critical points need to be chosen from the histogram as precondition, which determine the results of enhancement. But this is difficult in automatic image processing. Similarly, [PERSON] [12] introduced variational models for both the enhancement of gray-scale images and color images, which cannot automatically select the proper parameters either. [PERSON] [13] proposed an approach for low-light image based on illumination map estimation. But the method is not available for high dynamic range images because it may excessively enhance the bright targets. This paper presents an entropy-maximized adaptive histogram equalization (EMAHE) algorithm. By analyzing the specific characteristics of the source images, this algorithm can automatically select the best conversion parameters in order to achieve the optimal enhancement effect. Figure 1: Remote sensing image acquired by the Jilin-1 satellite. The multi-exposure fusion method uses different imaging parameters to obtain numerous images of the scene, attempting to record more details of the targets under different illumination conditions in the respective images. Then, using a specific algorithm it integrates the effective characteristics of these images into one image in order to show targets clearly together. [PERSON] [14] proposed a method that firstly calculates the brightness, contrast, and gradient. Then, he used the probability model to fuse the different exposure images. This method can preserve the information of different brightness targets in each image and suppress the inversion effect of the luminance gradient, but it ignores the preservation of entropy and average gradient. [PERSON] [15] proposed a direct fusion method, which is independent of the exposure times or the response function. But four parameters should be set properly when we use this method, and this brings some uncertainties. [PERSON] [16] firstly calculated the grey value and the regional contrast of images to obtain the initial fusion weight. Afterwards he applies the recursive filtering to optimize the weight. This algorithm is relatively simple. However, when smoothing the weight map, it cannot completely retain the useful information of the original image, and the edge retention is not so good. The pyramid decomposition of image is a multi-scale and multi-resolution decomposition method [17; 18; 19], which can highlight the important features and details of the image on different scales and spatial resolutions when used in image fusion. The common decomposition models include the Laplacian pyramid [17], contrast pyramid [18], gradient pyramid [19], and so on. Among them, the gradient pyramid decomposition can provide the sub-images in different directions of each dissociation layers, obtaining numerous edge characteristics. It can effectively combine the characteristics and details of source images together with further processing of the sub-images through different fusion operators, and it has good stability and noise immunity. After numerous contrast experiments, we find that if only one method of image enhancement or multiple exposure fusion is used, it is difficult to achieve the desired imaging results for the complex scenes with large contrast. In fact, the multi-exposure fusion algorithm has two key factors that determine the final effect. One is the acquisition and effective preprocessing of high-quality source images. The other is the selection of a reasonable fusion strategy and fusion operator. The quality of the source images directly determines the amount of valuable information that can be used in the fusion process. The fusion factors determine the extraction of the details from different images. In this paper, the entropy and average gradient are selected as clues in image enhancement when selecting and preprocessing the source images.