Aiming at the limitation of the dynamic range of the imaging sensor, the size of the local window and the fusion image method is further studied, and the multi-exposure image fusion method based on the camera response curve is improved. By changing the exposure time, a set of images with different exposure degrees is obtained, and image fusion is performed on the high-brightness image and the low-brightness image. Firstly, the conversion factor is directly calculated based on the image pixel value, which simplifies the calculation of the pixel ratio factor curve of the High Exposure (HE) image and Low Exposure (LE) image, and avoids the solution of the camera response curve. The pixel values in the low-brightness image are mapped to the pixel value range of the high-brightness image through the ratio factor, and then the image is subjected to local windowing processing. There are three cases of overexposure and good overexposure. For different exposure situations, according to the saturation of the neighborhood pixels in the highlighted image window, different weight coefficients are determined for multi-exposure weighted fusion, which is roughly divided into three steps:1) Select the unsaturated pixel value of the HE image and the corresponding pixel value in the LE image to linearly fit to obtain the pixel ratio factor curve.2) After adding local windows to the HE image, determine the saturation of the pixel values in each window. Whether the center pixel value of the highlighted image is saturated and whether the neighborhood pixel values of the center value are all saturated, the exposure of the center pixel value is determined. The situations are divided into three categories, 1) Good exposure: the central pixel value is not saturated, and all the neighborhood pixel value sets are not saturated; 2) Incomplete overexposure: the central pixel value is not saturated, the neighborhood pixel value set is not completely saturated or the central pixel When the value is saturated, at least one of the neighboring pixel value sets is saturated; 3) Complete overexposure: the center pixel value is saturated, and the neighborhood pixel value set is also saturated.3) According to the saturation situation, determine the weight coefficient of each pixel value fusion of the HE and the LE images. The weight coefficient is determined by the proportion of unsaturated pixel values in the neighborhood pixel value set in the HE image, and the final HDR image is obtained by weighted fusion.In terms of experimental verification, two typical multi-exposure fusion test sets of Bottle and Airport are selected to select the size of the local window and the imaging effect in a low signal-to-noise ratio environment. The wavelet transform fusion method and the window fusion method in this paper are compared horizontally. The experimental results show that:(1) With the increase of the selected window, the more pixels involved in the calculation, the influence of the over-bright central pixel value in the scene in the fusion process gradually decreases, the overall brightness decreases, and the quality of the fused image is more vulnerable. However, if the selection window is too small, the estimation accuracy of the saturated pixel value of the neighboring pixels will decrease. Therefore, in the selection of the window size, the influence of local noise on the quality of the fused image and the constraining ability of the neighboring pixels to the highlighted center pixel value should be considered at the same time. In order to achieve a better fusion effect, the algorithm in this paper selects the $\mathrm{5}\times \mathrm{5}$ window to fuse multiple images, improves the contrast of the image while maintaining the details of the image, and effectively restores the changes in the light and dark levels in the scene. (2) When the image signal-to-noise ratio is higher than 18 dB, the dark scene information in the LE image can still be effectively recovered after fusion, and the overall imaging effect of the fusion result can be guaranteed. There are two main reasons for the improvement of the anti-noise ability of the fused image: 1) The algorithm tries to use the pixel value in the highlighted image as much as possible, but the signal value of the highlighted image is generally too large, and the SNR of the pixel value after adding noise is still very large. 2) The pixel value of the low-brightness image used to replace the overexposed pixel value of the high-brightness image will also be larger, so the influence of noise on the pixel value will also be reduced. (3) Compared with other algorithms, the algorithm in this paper can not only keep the overall contrast accurate and the image undistorted but also restore the edge clarity, retain the dark information in the bright environment, and reduce the halo caused by the strong light source. At the same time, the fusion algorithm uses local windows to process pixel values. The calculations between each window are independent of each other, and it is not necessary to process pixel values with good exposure. Therefore, the unique values of independent operations between each window are expected to be realized on hardware platforms such as GPU. Thread parallel processing, with the potential to achieve HDR real-time fast imaging.