A still or video camera based on a Bayer-type image sensor is inherently an under-sampled system in terms of color pixel reconstruction. Accurate reconstruction of green channel information and minimization of color artifacts are two primary goals in the color demosaicing methods. Unsuccessful demosaicing methods usually come up with large color artifacts, particularly at image areas with fine details. In the proposed method, we first estimate green values at each chrominance pixel position by utilizing cubic convolution interpolation along the direction of the smallest gradient magnitude. We have defined a diamond shaped interpolation kernel and four different gradient directions to facilitate accurate reconstruction of the green channel. Reconstruction of chrominance channels comprises spectral correlation based averaging of neighboring chrominance pixels and a proposed sequential filtering on the econstructed chrominance channels. Due to the introduction of sequential filtering stage, conventional quantitative image quality measures such as PSNR or PESNR are not high but we found that the visual quality as observed from the human visual system is more natural and comfortably vivid reconstruction can be obtained. Moreover, the proposed demosaicing method comprises additions and subtractions for the most part, which makes its implementation more tractable.
The usage of cellular camera phones and digital cameras is rapidly increasing. but camera imaging application is not so expanded due to the lack of practical camera imaging technology. Especially the acquisition environments of camera images are very different from those of scanner images. The status of light condition, viewing distance and viewing angles constantly varies in case of cameras. The variations of light condition and viewing distance make it difficult to extract character areas from images through binarization and the variation of camera viewing angles makes the images distorted geometrically. Therefore, the extraction of character areas for camera document images is far more complex and difficult than for scanner images.
In this paper, these problems are totally discussed and the resolving methods are suggested for better image recognition. The solutions such as adaptive binarization, color conversion, correction of lens distortion and geometrical distortion correction are discussed and the correction methods are suggested for accurate document image recognition. In experiment, we use the various types of document images captured by mobile phone cameras and digital cameras. The results of distortion correction show that our image processing methods are efficient to increase the accuracy of character recognition for camera based document image.