Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

Demosaicing is a technique that reconstructs an RGB image from fragmentary color samples sensed by the image sensor. The color filter array (CFA), which is placed over the image sensor, determines the color of each pixel. The most used color filter array is the Bayer CFA. This paper proposes an Anti-Aliasing and Anti-Color-Artifact Demosaicing (AAACA) algorithm for the Bayer pattern and the resource-efficient very-large-scale integration (VLSI) architecture for the proposed algorithm. The AAACA comprises an anti-aliasing approach and a color artifacts filter named color difference-based median filter (CDMF). Compared to the traditional demosaicing methods that equally treat pixels on and not on edges, the AA reconstructs the pixels on edges with different strategies from those not on edges, significantly removing the aliasing around recovered edges. Then the CDMF is utilized to remove the color artifacts of the reconstructed RGB image based on the color difference after median filtering. We respectively simulate the quantitative evaluation and subjective visual quality on McMaster and Kodak datasets. Our experiments reveal that the proposed AAACA algorithm can significantly remove the visual aliasing around recovered edges and greatly reduce color artifacts in demosaiced images compared to the state-of-art demosaicing algorithms. The proposed VLSI architectures can achieve superior visual qualities compared with the previous VLSI implementations under the same process technology conditions with 180nm CMOS technology, an image resolution of $1280\times 720$ (HD), and a working frequency of 200MHz.

I. Introduction

Demosaicing refers to the process of constructing a full RGB image from fragmentary color samples captured by the image sensor of a digital camera. The color filter array (CFA) determines the missing colors placed over the image sensor. For different CFAs, the demosaicing algorithms are different. The Bayer CFA [1] (Fig. 1) is the most commonly-used pattern. Fig. 1.

Four cases of the masks in Bayer CFA and Quad Bayer CFA.