In this paper, we propose a chromatic aberration (CA) correction algorithm based on a false color filtering technique. In general, CA produces color distortions called color fringes near the contrasting edges of captured images, and these distortions cause false color artifacts. In the proposed method, a false color filtering technique is used to filter out the false color components from the chroma-signals of the input image. The filtering process is performed with the adaptive weights obtained from both the gradient and color differences, and the weights are designed to reduce the various types of color fringes regardless of the colors of the artifacts. Moreover, as preprocessors of the filtering process, a transient improvement (TI) technique is applied to enhance the slow transitions of the red and blue channels that are blurred by the CA. The TI process improves the filtering performance by narrowing the false color regions before the filtering process when severe color fringes (typically purple fringes) occur widely. Last, the CA-corrected chroma-signal is combined with the TI chroma-signal to avoid incorrect color adjustment. The experimental results show that the proposed method substantially reduces the CA artifacts and provides natural-looking replacement colors, while it avoids incorrect color adjustment.

De-interlacing based on motion compensation (MC) is one of the best ways of improving the resolution of a progressive video converted from an interlaced source. However, the converted frames often suffer from serious defects like feathering artifacts in regions with inaccurate motion vectors (MVs). In such regions, an intra-field method that is robust to MV errors can be used to correct motion compensation artifacts (MCAs). In this letter, we propose an adaptive arbitration method to combine intra-field and MC methods adequately. The proposed method considers the reliability of MC results along with the MV reliability measured by the spatio-temporal consistency of MVs and displaced pixel differences. The MC reliability is determined by detecting MCAs in MC results, and then the MV reliability is adjusted according to the MC reliability. Also, adaptive-weight MC and pseudo MC methods are proposed to provide more reliable MC results and to improve the accuracy of MCA detection, respectively. Experimental results show that the proposed method provides high-quality video sequences while reducing many visible artifacts.