There is often substantial noise and blurred details in the images captured by cameras. To solve this problem, we propose a novel image enhancement algorithm combined with an improved lateral inhibition network.First, we built a mathematical model of a lateral inhibition network in conjunction with biological visual perception; this model helped to realize enhanced contrast and improved edge definition in images. Next, we proposed that the adaptive lateral inhibition coefficient adhere to an exponential distribution thus making the model more flexible and more universal. Finally, we added median filtering and a compensation measure factor to build the framework with high pass filtering functionality thus eliminating image noise and improving edge contrast, addressing problems with blurred image edges. Our experimental results show that our algorithm was able to eliminate noise and the blurring phenomena and enhance the details of visible and infrared images.Image enhancement is an indispensable technique and essential method of improving image quality in digital image processing. Even with the ubiquity of digital cameras and mobile telephones, there are substantial amounts of noise in images because of camera defocusing, a lack of uniform illumination, atmospheric disturbances, and the like not clear edge texture and producing dark or highlight area, i.e., which have a great impact on executing missions. Hence, it