A content-aware image editing method for perceptual size restoration based on seam carving

To alleviate the gap between the perceptual size of Thing of Interest (ToI) in a real scene and its appearance in a photograph, ToI region enlargement methods based on seam carving have been proposed in previous studies. However, state-of-the-art methods may suffer from issues such as a lack of automation and distortion caused by seam concentrations. In this study, we propose an image editing method for ToI enlargement based on seam carving. The proposed method incorporates the Segment Anything Model to automatically generate ToI mask. To address the issue of seam concentrations, a wide-area energy strategy is introduced. Additionally, to improve the image quality of the enlarged ToI region, a super-resolution-based post-processing mechanism is put forward. Subjective experiments were conducted to evaluate the effectiveness of the proposed method. The experimental results suggest that the proposed method effectively enlarges the ToI region while suppressing distortion caused by seam concentrations.