Zilai Gong, Masayuki Tanaka, Yusuke Monno, and Masatoshi Okutomi
Tokyo Institute of Technology, Tokyo, Japan
IEEE International Conference on Image Processing (ICIP 2022)
Most consumer digital cameras employ a single-chip image sensor with a color filter array (CFA), where the purpose of an in-camera imaging pipeline is to generate a noise-free and color-corrected standard RGB image from mosaic CFA RAW data. The joint design of camera spectral sensitivity (CSS) and the imaging pipeline has great potential to derive better imaging quality. However, since there is a trade-off between the robustness to noise and the accuracy of color reproduction, one fixed CSS cannot realize optimal imaging in terms of both aspects under various noise levels. Thus, in this paper, we propose noise-aware imaging using camera prefilter for each noise level, where we jointly design the spectral sensitivity of the prefilters, that of CFA, and imaging networks to realize optimal imaging in all noise levels. Experimental results under various noise levels demonstrate that our imaging method using the prefilters outperforms existing methods based on a fixed CSS.
In this paper, we propose a novel method for optimal noise-aware imaging using camera prefilters, which are easily switchable in front of the camera lens according to each noise-level condition, as illustrated in Fig.1.
To derive optimal imaging based on a deep learning framework, we have proposed a method for jointly designing the spectral transmittance of the prefilters, the CSS of the CFA, and the imaging networks associated with each prefilter.