Abstract
It is commonly believed that having more white pixels in a color filter array (CFA) will help the demosaicing performance for images collected in low lighting conditions. However, to the best of our knowledge, a systematic study to demonstrate the above statement does not exist. We present a comparative study to systematically and thoroughly evaluate the performance of demosaicing for low lighting images using two CFAs: the standard Bayer pattern (aka CFA 1.0) and the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using the clean Kodak dataset containing 12 images, we first emulated low lighting images by injecting Poisson noise at two signal-to-noise (SNR) levels: 10 dBs and 20 dBs. We then created CFA 1.0 and CFA 2.0 images for the noisy images. After that, we applied more than 15 conventional and deep learning based demosaicing algorithms to demosaic the CFA patterns. Using both objectives with five performance metrics and subjective visualization, we observe that having more white pixels indeed helps the demosaicing performance in low lighting conditions. This thorough comparative study is our first contribution. With denoising, we observed that the demosaicing performance of both CFAs has been improved by several dBs. This can be considered as our second contribution. Moreover, we noticed that denoising before demosaicing is more effective than denoising after demosaicing. Answering the question of where denoising should be applied is our third contribution. We also noticed that denoising plays a slightly more important role in 10 dBs signal-to-noise ratio (SNR) as compared to 20 dBs SNR. Some discussions on the following phenomena are also included: (1) why CFA 2.0 performed better than CFA 1.0; (2) why denoising was more effective before demosaicing than after demosaicing; and (3) why denoising helped more at low SNRs than at high SNRs.
Highlights
The standard Bayer pattern [1], known as color filter array (CFA) 1.0, has been widely used in many commercial cameras
PWerefoarmdoapncteedMtehteriwcsell-known denoising algorithm known as BM3D (Block Matching 3D) [54] in our dIenntohisisinpgaepxepr,erwime ehnatvs.e used five performance metrics to compare the different methods and CFA patterns. 3.2
We investigate the performance of CFAW1e.0hianvme o1r6e mreeatlhisotdicslofowr ldigehmtionsgaiccoinngdiCtioFnAs o1.f02. 0TdhBes.F3SimfuisliaorntomSeetchtoiodn f3u.3se.1s, wthee arlessouhltasveof thDreeme sounbe-tc,aAseRsI., and LDI, which are the best performing demosaicing methods
Summary
The standard Bayer pattern [1], known as color filter array (CFA) 1.0, has been widely used in many commercial cameras. Because of the success of the Bayer pattern, a follow-up pattern, known as red-green-blue-white (RGBW) or CFA 2.0, was introduced by researchers at Kodak [7,8]. For each 4 × 4 block in a RGBW pattern (Figure 1b), there are 50% white pixels, 25% green pixels, and 12.5% red and blue pixels. IInn tthhee CCFFA research commmmuunniittyy, one common belieeff iiss thhaatt CCFFA 2.0 has better performance for images taken in low lighting conditions. If one collectss imaaggeess iinn loww lighhttiinngg ccoonnddiittiioonnss,, tthheenn wwee mmaayy nnoott hhaavvee tthhee ggrroouunndd ttrruutthh iimmaaggeess,, which would be used to generate objective metrics. IInn [[1177]],, llooww ligghhttiinngg iimmaaggeess wweerree emuullaatteedd bbyy adddiinngg noiissee to clean images. TThhiirrdd,, aafftteerr tthhee ddeemmoossaaiicciinngg ooff llooww lliigghhttiinngg iimmaaggeess,, the demosaiced images are still noisy.
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