Review of Bayer Pattern Color Filter Array (CFA) Demosaicing with New Quality Assessment Algorithms

摘要

To address the frequent lack of a reference image or ground truth when performance testing Bayer pattern color filter array (CFA) demosaicing algorithms, we propose two new no-reference quality assessment algorithms. These new algorithms give a relative comparison of two demosaicing algorithms by measuring the presence of two common artifacts in their output images. For this purpose, we reviewed various demosaicing algorithms, especially adaptive color plane, gradient-based methods, and median filtering, paying particular attention to the false color and edge blurring artifacts common to all demosaicing algorithms. We also reviewed classic quality assessment methods that require a reference image (MSE, PSNR, and deltaE), characterized their typical usage, and identified their associated pitfalls. With this information in mind, the motivations for no-reference quality assessment are discussed. From that, we designed new quality assessment algorithms to compare two images demosaiced from the same CFA data by measuring the sharpness of the edges and determining the presence of false colors. Using these new algorithms, we evaluated and ranked the previously described demosaicing algorithms. We reviewed a large quantity of real images, which were used to justify the rankings suggested by the new quality assessment algorithms. This work provides a path forward for future research investigating possible relationships between CFA demosaicing and color image super-resolution.