Traditional relative radiometric normalization methods generally depend on global statistical linear parameters, do not consider two-dimensional radiometric distribution, and do not eliminate foreground objects in an image. Thus, we present a method for relative radiometric consistency process based on object-oriented smoothing and contourlet transforms. Object-level smoothing is applied to both the reference image and the image to be normalized so as to reduce the influence of foreground objects on background radiation extraction. Then, high-frequency and low-frequency sections of an image are separated by contourlet transforms to preserve high-frequency texture information of the image to be normalized, with low-pass filtering applied to the low-frequency sections to gather the background radiation difference. Finally, contourlet reverse transforms are used to reconstruct the radiometrically normalized images. Test results show that the proposed method is effective for radiometric normalization of images with both large-scale and small-scale radiometric characteristics. The proposed method can not only normalize linear and nonlinear radiation differences at the same time, but also maximally preserve image texture information. It can improve the visual effects of normalized images and increase change detection accuracy.