Nonlinear demosaicking method and apparatus for nonlinear CMOS image sensors exhibiting low-density salt-and-pepper noise

Abstract

Simple demosaicking methods designed for linear CMOS image sensors, such as MATLAB’s demosaic function, may be used with a monotonic nonlinear sensor having a Bayer color filter array (CFA). However, such methods may be inadequate at handling dynamic salt-and-pepper noise (SPN), i.e., outlier pixels, which is expected in images taken with a nonlinear sensor. Although SPN is present with linear sensors, nonlinear sensors express low-density light-dependent SPN that requires filtering. Extending a recent work on dynamic SPN filtering of a nonlinear sensor, we propose, evaluate, and verify a nonlinear method and apparatus to demosaic images, taken with a Bayer CFA, while simultaneously filtering the SPN. The approach relies on the use of weighted medians to filter the SPN, especially at densities that imply isolated outliers in small neighborhoods, while determining an accurate red, green, and blue (RGB) color at every pixel location. For explanatory purposes, three variants of the proposed method are presented and evaluated. A ground-truth image set, in which RGB channels were not obtained by demosaicking, is subsampled in a Bayer CFA pattern to produce mosaicked images for testing. In varying densities, SPN is introduced to these Kodak images for method and apparatus evaluation. Results of the proposed method and its variants are compared with those obtained with MATLAB’s demosaic function. Considering the alternatives and also apparatus complexity, the proposed nonlinear demosaicking method proves superior in visual quality, for smooth textures and along edges, and in peak signal-to-noise ratio, when there is low-density SPN.