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This paper presents an intra field de-interlacing algorithm for spatial edge preserving using the detection of accurate edge direction. Conventional intra field de-interlacing algorithms determine the edge direction at the pixel or half pixel level, so that they can be highly sensitive to noise and lead to image degradation. In this paper, the proposed algorithm first considers the edge tendency in the edge region, and then candidate direction vectors (CDVs) are selected using the modified Sobel operation through the edge tendency. Finally, the CDVs are adaptively applied to interpolate the lost pixel in the edge region. Experimental results show that the proposed algorithm performs well with a variety of still and moving images compared with conventional intra field algorithms in the literature.

This paper presents a novel intra deinterlacing algorithm (NID) based on content adaptive interpolation. The NID consists of three steps: pre-processing, content classification, and content adaptive interpolation. There are also three main interpolation methods in our proposed NID, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the NID method performs better than previous techniques.

This paper presents a block loss recovery technique for the image block data corrupted by transmission losses through the employment of fine directional interpolation (FDI). The proposed algorithm introduces a spatial direction vector (SDV). The SDVs are extracted from the edge information of the neighboring image data. Subsequently, the SDVs are adaptively applied to interpolate lost pixels on a pixel-by-pixel basis. This approach improves the capability to more reliably recover high-detailed contents in the corrupted block. Experimental results demonstrate that the FDI method performs better as compared to previous techniques