Image editing based on Sparse Matrix-Vector multiplication

This paper presents a unified model for image editing in terms of Sparse Matrix-Vector (SpMV) multiplication. In our framework, we cast image editing as a linear energy minimization problem and ad dress it by solving a sparse linear system, which is able to yield a globally optimal solution. First, three classical image editing operations, including linear filtering, resizing and selecting, are reformulated in the SpMV multiplication form. The SpMV form helps us set up a straightforward mechanism to flexibly and naturally combine various image features (low-level visual features or geometrical features) and constraints together into an integrated energy minimization function under the L₂ norm. Then, we apply our model to implement the tasks of pan-sharpening, image cloning, image mixed editing and texture transfer, which are now popularly used in the field of digital art. Comparative experiments are reported to validate the effectiveness and efficiency of our model.