Objective During laparoscopic surgery, smoke and fog can affect the doctor""s observation in the operative field. Compared to special smoke or fog removal devices, image processing algorithms can simultaneously achieve desmoking and defogging for laparoscopic images, with low cost, simple operation, and ease of use. However, the current laparoscopic defogging algorithm still has problems such as color cast, lack of details, and low brightness after processing. Method In response to the problems of laparoscopic image de-fogging, this paper proposes a laparoscopic image de-fogging algorithm based on intelligent perception of fog density, which includes an image enhancement module based on intelligent perception of transmittance and an image enhancement module based on perception of illumination components. The image enhancement module with intelligent transmittance sensing can optimize the defogging effect by sensing the specific pixel fog content of the image. The image enhancement module with light component perception enhances the brightness of the image based on the light in different pixel regions of the image, allowing the image to improve its brightness without over-enhancing. Results 100 images with different scenes at 4K resolution and 1080P resolution were selected for verification. The non-reference fog density evaluation index showed that the proposed intelligent perception algorithm reduced the fog density by 67.3%, 68.1%, and 14.2% compared to the three control algorithms, respectively. Conclusion The intelligent perception algorithm effectively improves the enhancement effect of laparoscopic image defogging.