A Color Filter Array Interpolation Method Based on Sampling Theory

• Main Author: Glotzbach, John William
• Format: Others
• Language: en_US
• Published: Georgia Institute of Technology 2005
• Subjects: Sampling; Aliasing; Digital signal processing; Color image processing; Color filter array; Image interpolation
• Online Access: http://hdl.handle.net/1853/4785

Digital cameras use a single image sensor array with a color filter array (CFA) to measure a color image. Instead of measuring a red, green, and blue value at every pixel, these cameras have a filter built onto each pixel so that only one portion of the visible spectrum is measured. To generate a full-color image, the camera must estimate the missing two values at every pixel. This process is known as color filter array interpolation. The Bayer CFA pattern samples the green image on half of the pixels of the imaging sensor on a quincunx grid. The other half of the pixels measure the red and blue images equally on interleaved rectangular sampling grids. This thesis analyzes this problem with sampling theory. The red and blue images are sampled at half the rate of the green image and therefore have a higher probability of aliasing in the output image. This is apparent when simple interpolation algorithms like bilinear interpolation are used for CFA interpolation. Two reference algorithms, a projections onto convex sets (POCS) algorithm and an edge-directed algorithm by Adams and Hamilton (AH), are studied. Both algorithms address aliasing in the green image. Because of the high correlation among the red, green, and blue images, information from the red and blue images can be used to better interpolate the green image. The reference algorithms are studied to learn how this information is used. This leads to two new interpolation algorithms for the green image. The red and blue interpolation algorithm of AH is also studied to determine how the inter-image correlation is used when interpolating these images. This study shows that because the green image is sampled at a higher rate, it retains much of the high-frequency information in the original image. This information is used to estimate aliasing in the red and blue images. We present a general algorithm based on the AH algorithm to interpolate the red and blue images. This algorithm is able to provide results that are on average, better than both reference algorithms, POCS and AH.