CNN-based image small-angle rotation angle estimation

Digital image rotation angle estimation is an essential research branch of resampling detection, and many methods have been proposed for the resampling detection problem. However, the angle factor estimation for small-angle image rotation has not been adequately studied. This paper proposes a digital image rotation angle estimation algorithm based on a convolutional neural network (CNN). The angle range of small-angle rotations is defined as an integer interval from 1° to 9°. Thus, the problem of estimating the rotation angle of a digital image is converted into a multiclassification problem. The rotation image is first preprocessed with a high-pass filter, and the resampling trace of the image is enhanced. The two-dimensional cyclic spectrum of the filtered image is then calculated. As an input to the network model, the two-dimensional spectrum map suppresses the effect of image content on rotation angle estimation. Next, a series of convolution layers are used to automatically extract the resampling features caused by rotation from the two-dimensional cyclic spectrum. Finally, the rotation angle of the image is estimated in the output layer. Experimental results show the superiority of the proposed method in small-angle rotation image estimation.