Advances on image interpolation based on ant colony algorithm
This is an incremental improvement for image processing applications, offering a specific enhancement in interpolation techniques.
The paper tackles image interpolation for high-resolution scaling by proposing an ant colony algorithm with global weighting (AACA), which outperforms previous methods like OBACA that use local weighting, as shown in experimental results.
This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high-resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses a local weighting scheme. The strength of the proposed global weighting of the AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.