Unified Angle Adjustment Network for Image Composition Enhancement

We propose an angle adjustment algorithm for the composition enhancement of digital photographs. The proposed algorithm jointly learns the scene type, composition, and semantic line information of an image to improve the accuracy of angle adjustment. To this end, we design a unified angle adjustment network (UAAN), which consists of a unified encoder and four task-specific refinement modules and estimators. First, we generate shared features using the unified encoder. Then, we refine those features using the refinement modules to perform the four tasks of angle regression, scene type classification, composition classification, and semantic line detection. Experimental results demonstrate the effectiveness of the proposed UAAN algorithm.