Design of practical color filter array interpolation algorithms for digital cameras .2

Abstract

Single-chip digital cameras use a color filter array and subsequent interpolation strategy to produce full-color images. While the design of the interpolation algorithm can be grounded in traditional sampling theory, the fact that the sampled data is distributed among three different color planes adds a level of complexity. Previous ways of treating this problem were based on computationally intensive approaches, such as iteration. Such methods, while effective, cannot be implemented in today's crop of digital cameras due to the limited computing resources of the cameras and the accompanying host computers. These previous methods are usually derived from general numerical methods that do not make many assumptions about the nature of the data. Significant computational economies, without serious losses in image quality, can be achieved if it is recognized that the data is image data and some appropriate image model is assumed. To this end, the design ofpractical, high-quality color filter array interpolation algorithms based on a simple image model is discussed.

Vector Concepts-Based Spectral Modelling

“…For a non-zero parameter γ, two-component equations (7)- (9) can be re-written to obtain normalized color-ratios [8], [9]: (7)- (9) the proposed model becomes equivalent to the color-difference model [6]:…”

“…This is done by enforcing a spectral model during the demosaicking process. The most commonly used solutions are the the color-difference model [6], the colorratio model [7] and its normalized variants [8], [9]. Operating over spatially neighboring color pixels, the colordifference model is used to guarantee the minimization of the magnitude error, whereas the color-ratio model is based on the assumption of hue constancy in localized image regions.…”

Spatially adaptive color filter array interpolation for noiseless and noisy data

“…There are two basic interplane correlation models: the color difference rule (Laroche and Prescott, 1994;Hamilton and Adams, 1997) and the color ratio rule (Kimmel, 1999;Lukac et al, 2004a). The first model asserts that intensity differences between red, green, and blue channels are slowly varying, that is the differences between color channels are locally nearlyconstant (Laroche and Prescott, 1994;Hamilton and Adams, 1997;Adams, 1998;Plataniotis, 2004a, 2005b;Hirakawa and Parks, 2005a;Li, 2005;Zhang and Wu, 2005). Thus, they contain low-frequency components only, making the interpolation using the color differences easier (Hirakawa and Parks, 2005a).…”

“…In addition, there are approaches based on the following: pattern recognition (Cok, 1994), restoration algorithms (Taubman, 2000;Trussel et al, 2005), sampling theory (Adams, 1998) [see (Gunturk et al, 2005) for more details], the regularization theory (Keren and Osadchy, 1999), the Bayesian approach (Brainard, 1994;Vega et al, 2005), demosaicing in frequency domain (Alleysson et al, 2005;Dubois, 2005). Taubman (2000) proposed an efficient preconditioned approach of Bayesian demosaicing that is used in some digital cameras today.…”

“…We will classify the demosaicing techniques into two categories: noniterative (Laroche and Prescott, 1994;Hamilton and Adams, 1997;Adams, 1998;Taubman, 2000;Pei and Tam, 2003;Malvar et al, 2004;Zhang and Wu, 2005;Menon et al, 2007) and iterative (Kimmel, 1999;Gunturk et al, 2002;Lukac et al, 2004a;Hirakawa and Parks, 2005a;Li, 2005). There are also alternative ways of this classification, for instance considered in (Gunturk et al, 2005).…”