The absorption and scattering of light by marine water and suspended particles lead to the distortion of color in underwater microscopic images. This paper presents an improved cycle generative adversarial network （CycleGAN） algorithm for effectively correcting the color of microscopic images of underwater targets. The structural similarity index （SSIM） loss function， which measures the loss of color information among images， of the R， G， and B channels was added between the original underwater images and the reconstructed images. Therefore， the color of the R， G and B channels was accurately regulated. This enhanced not only the overall performance of the CycleGAN， but also the quality of images produced by the generator. Subsequently， the improved network was trained by using a training data set， which consisted of underwater multicolor self-made target images and microscopic images of natural stones. The trained network model was used to correct the color of the microscopic images of underwater stones. The results showed that the improved CycleGAN algorithm had distinct advantages in color correction over other methods. The peak signal-to-noise ratio and SSIM of the images processed by using this algorithm were 41.85% and 35.62% higher than those processed by using the traditional Retinex algorithm， respectively. Moreover， in terms of subjective vision， the corrected underwater microscopic images had the highest color similarity with the images taken in air. In conclusion， this method can effectively correct the color of underwater target images and improve the quality of underwater microscopic images. It can be applied in marine geology and marine biology.