The effect of a small field of view of microscopic images can be improved by stitching corneal nerve images. Owing to the vignetting effect of microscopic images， the stitched images can produce artifacts at the stitch site， affecting the diagnosis. To solve the problem of vignetting artifacts in stitched images， this study presents a method for correcting image vignetting by using nonlinear polynomial function modeling. First， a vignetting model is established for a single corneal neural image， constraints consistent with the physical properties of the vignetting are set， and the parameters of the vignetting model are iteratively optimized using the Levenberg–Marquardt optimization algorithm. During each optimization iteration， the logarithmic information entropy is calculated to determine the correction effect of the current vignetting model and prevent overcorrection of the image. At the end of the iterative optimization， the vignetting model is reversed to compensate for the original image and complete the vignetting correction process. A comparison of the stitched images before and after correction reveals that the corrected images have no obvious vignetting artifacts at the stitch site. Experiments on the images of five patient groups show that the mean values of the mean squared error， peak signal-to-noise ratio， and structural similarity evaluation indices of the corrected images reach 0.004 2， 72.225 1， and 0.960 0， respectively， with the best correction effect. The correction effect of the proposed algorithm is significantly better than that of other similar algorithms. The proposed method can effectively correct corneal image vignetting effects without cameras or environmental brightness parameters being fixed in advance. The corrected-image stitching effect is good； corneal-nerve stitching images that are more accurate and clearer with a larger field of view can be obtained.