When stitching images with weak contrast, there will be a few matching feature points distributed on the images to be stitched because of poor contrast and other factors and the image registration error will be high. To address this problem and improve the quality of image stitching, this study proposes an image stitching algorithm based on contrast limited adaptive histogram equalization (CLAHE) and improved zero-mean normalized cross-correlation (ZNCC). Before feature point extraction, we use the CLAHE algorithm to preprocess the weak contrast image for enhancing the image contrast, which increases number of matching points. Thereafter, the improved ZNCC algorithm combined with the main direction of the gradient of feature points is used to filter feature points, which improves the correct matching rate of feature points. Finally, we use the filtered feature points to calculate the transformation matrix and complete the image stitching. The experimental results indicate that compared with other algorithms, the proposed algorithm increases number of correct matching points by approximately 25% in the weak contrast image and reduces the false matching rate by 0.5 percentage points?3 percentage points compared with the SIFT algorithm, effectively improving the image registration accuracy, reducing the registration ghosting, and optimizing the image mosaic results.