Objective: Haze is a common natural phenomenon formed by suspended particles in the atmosphere (e.g., water droplets and dust). In foggy weather, images obtained outdoors lose contrast and exhibit color distortion. Therefore, these images are difficult to utilize in computer vision applications, such as object detection and target tracking. One of the reasons images captured outdoors exhibit the above mentioned problems is that the reflected light received by the camera is attenuated. Another reason is that the irradiance from these objects blends with the atmospheric light scattered by particles. Therefore, effective methods must be used to remove haze. Single-image haze removal has exhibited remarkable progress recently. Dark channel prior is one of the valid methods of haze removal. Most images applied with dark channel prior produce good results. However, the dark channel prior method uses a soft matting to refine transmission maps, thus increasing the complexity of the algorithm. The guided filter is used to optimize transmission maps in dark channel prior. However, dark channel prior may still lead to the problem of cross color because the estimated transmission of the sky or bright-object regions in a hazy image is undervalued. Similarly, most existing methods of image dehazing cannot deal with this problem well. To resolve this deficiency, a method is proposed in this study to remove haze from an image through dark channel prior and a guided filter. Method: First, with the model of dark channel prior, a coarse estimate of atmosphere veil is obtained. Second, the atmosphere veil containing sky or bright-object regions of the hazy image is corrected by introducing a correction function because dark channel prior is inapplicable to bright regions. Third, to smooth the edge and retain the detail information of the image, a guided filter is utilized to optimize the coarse atmosphere veil. The initial transmission map is obtained from the optimized atmosphere veil and optimized by the guided filter. Finally, the optimized transmission map and the estimated atmospheric light are used to obtain the restored image. Result: To demonstrate the effectiveness of the proposed method, several classic images are used to conduct experiments. Peak signal-to-noise ratio (PSNR) and mean squared error (MSE) are adopted to measure the degree of distortion of the experimental results. The experimental results show that the haze-free image recovered with the proposed method produces a better result for non-bright regions and retains the original color of bright-object regions compared with Tarel’s method, He’s method, Meng’s method, and Jiang’s method. In general, the proposed method presents minimum distortion. Compared with He’s method, the PSNR of the proposed method is increased by 0.6005 dB, MSE is reduced by 0.0026, and operation time is reduced by 29.6220 s for an image with a size of 460?×?300. Conclusion: This study proposed a method to remove haze from an image that includes sky or bright-object regions. The method uses dark channel prior and a guided filter. Subjective and objective evaluations show that the proposed method produces good results for sky or bright-object regions. The method addresses the problem of cross color in bright regions caused by dark channel prior. Compared with that of He’s method, the operation time of the proposed method is shorter.