title: Adaptive homogeneity-directed demosaicing algorithm.
abstract: A cost-effective digital camera uses a single-image sensor, applying alternating patterns of red, green, and blue color filters to each pixel location. A way to reconstruct a full three-color representation of color images by estimating the missing pixel components in each color plane is called a demosaicing algorithm. This paper presents three inherent problems often associated with demosaicing algorithms that incorporate two-dimensional (2-D) directional interpolation: misguidance color artifacts, interpolation color artifacts, and aliasing. The level of misguidance color artifacts present in two images can be compared using metric neighborhood modeling. The proposed demosaicing algorithm estimates missing pixels by interpolating in the direction with fewer color artifacts. The aliasing problem is addressed by applying filterbank techniques to 2-D directional interpolation. The interpolation artifacts are reduced using a nonlinear iterative procedure. Experimental results using digital images confirm the effectiveness of this approach.
authors.affiliation: Cornell University, Ithaca, NY 14850, USA. kh237@cornell.edu
authors.forename: Keigo
authors.initials: K
authors.lastname: Hirakawa
authors.forename: Thomas W
authors.initials: TW
authors.lastname: Parks
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003116:Color; D003124:Colorimetry / Q000379:methods*; D003196:Computer Graphics; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted