Partial differential equation-based approach for removal of chromatic aberration with local characteristics

We propose a chromatic aberration (CA) reduction technique that removes artifacts caused by lateral CA and longitudinal CA, simultaneously. In general, most visible CA-related artifacts appear locally in the neighborhoods of strong edges. Because these artifacts usually have local characteristics, they cannot be removed well by regular global warping methods. Therefore, we designed a nonlinear partial differential equation (PDE) in which the local characteristics of the CA are taken into account. The proposed algorithm estimates the regions with apparent CA artifacts and the ratios of the magnitudes between the color channels. Using this information, the proposed PDE matches the gradients of the edges in the red and blue channels to the gradient in the green channel, which results in an alignment of the positions of the edges while simultaneously performing a deblurring process on the edges. Experimental results show that the proposed method can effectively remove even significant CA artifacts, such as purple fringing as identified by the image sensor. The experimental results show that the proposed algorithm achieves better performance than existing algorithms.