Aiming at the problem that the background noise is excessively enhanced by the existing panchromatic remote sensing image enhancement algorithms when the object is enhanced in the image, a block image enhancement method based on global adaptive processing is proposed. Firstly, block processing is carried out and image enhancement parameters of each image block in the image are calculated respectively. Remote sensing images contain many types of ground objects, and the difference between different ground objects is great. If unified enhancement parameters are used to enhance the whole image, the enhancement effect of some ground objects is often not ideal. Local enhancement can solve the above problems well. This method mainly applies to a small area near the target object, and can fully use the image's dynamic range to represent the change of image grayscale. Next, global adaptive processing is carried out. Global adaptive enhancement parameters are calculated and used to modify the enhancement parameters of noise blocks. This paper proposes a global adaptive enhancement technology based on a grayscale histogram for global adaptive processing of locally enhanced images, which can obtain the grayscale distribution of background objects in the image. This method helps to determine the brightness of the target relative to the background, and after determining the relative brightness, the enhancement parameters with better enhancement effect can be determined. Then, the adjacent image blocks are merged by building block difference factor, and the image blocks are classified as detail blocks and noise blocks. Local block enhancement will lead to more highlighting noise in some image blocks. In order to accurately identify these image blocks and use global adaptive enhancement parameters based on grayscale histogram to correct these noise blocks, this study calculated the block difference factor between adjacent image blocks. According to this, the information of texture and brightness between adjacent image blocks can be judged whether the abrupt change occurs at the boundary of the block. Finally, the parameters of each pixel are obtained by interpolating the enhancement parameters based on image blocks, and panchromatic remote sensing images are enhanced according to these parameters. The proposed method is used for panchromatic remote sensing images with different scenes, and the effects of various enhancement methods are evaluated based on various indexes. It is found that the proposed enhancement methods have good performance. By comparing the enhancement results of different algorithms for remote sensing images near ports, it can be seen that the proposed algorithm can reduce the dynamic range of images and effectively enhance the details of objects in the image, which can improve the clarity of ship objects in remote sensing images near ports. By comparing the enhancement results of cloud coverage images by different methods, it can be seen that the proposed algorithm can effectively suppress cloud interference on ship targets in the images. Even if some ships are covered by clouds, the contrast of grayscale of the image pixels around the target can be increased as much as possible by this algorithm, which corrects the shortcomings of over-enhancement or under-enhancement in the cloud covered image processing by previous methods. This indicates that the proposed algorithm can effectively solve the problem of over-enhancement of background noise while enhancing the details of target objects in the image by existing panchromatic remote sensing image enhancement algorithms. The average running time of different algorithms is also calculated, and it is found that the average running time of the proposed algorithm is 0.14 s, which is slightly less than the image processing time of the contrast enhancement method. The method can eliminate the residual error of the existing radiometric correction processing to a certain extent and make the same object in different images comparable.