Abstract: Infrared imaging often suffers from reduced contrast owing to reflection and scattering of infrared light by suspended particles and fog. To address this issue, an infrared image enhancement method combining dark channel restoration and piecewise equalization is proposed. According to the atmospheric scattering model, the infrared image is divided into non-high-brightness and high-brightness regions. The transmittance of the non-high-brightness region is estimated using the dark channel prior, whereas that of the high-brightness region is estimated using the dark channel prior with an adjusted parameter. Additionally, atmospheric light is estimated from the maximum pixel value of a Gaussian-filtered image to restore the infrared image. The grayscale of the restored image is then divided into three parts with equal histogram frequencies, and histogram equalization is applied within each subspace. Experimental results demonstrate that the proposed method enhances visual quality of infrared images more effectively than existing approaches. Specifically, the average gradient, information entropy, and coefficient of variation obtained using the proposed method increased by 11.8%, 1.38%, and 8.13%, respectively, compared with current methods. These results demonstrate the superiority of the proposed infrared image enhancement technique.