Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

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.The baseline approach refers to a simple upsampling of the reduced resolution color image shown in Figure 2. The standard approach for CFA 2.0 is shown in Figure 2, which illustrates how to combine the interpolated luminance image with the reduced resolution color image to generate a full resolution color image. Electronics 2019, 8, 1444 4 of 56 combine the interpolated luminance image with the reduced resolution color image to generate a full resolution color image.Figure 2. Standard approach to demosaicing CFA 2.0 images. Image from [38].In the paper [16] written by us, we proposed a pansharpening approach to demosaicing CFA 2.0. This approach is illustrated in Figure 3. The missing pixels in the panchromatic band are interpolated. At the same time, the reduced resolution CFA is demosaiced. We then apply pansharpening to generate the full resolution color image. There are many pansharpening algorithms that can be used. Principal Component Analysis (PCA) [39], Smoothing Filter-based Intensity Modulation (SFIM) [40], Modulation Transfer Function Generalized Laplacian Pyramid (GLP) [41], MTF-GLP with High Pass Modulation (HPM) [42], Gram Schmidt (GS) [43], GS Adaptive (G...

Demosaicing of CFA 3.0 with Applications to Low Lighting Images

“…The baseline approach is a simple demosaicing operation on the CFA, followed by an upsampling of the reduced resolution color image shown in Figure 2 of [ 13 ]. The standard approach consists of four steps as shown in Figure 2 of [ 13 ]. Step 1 interpolates the luminance image with half of the white pixels missing.…”

“…In our recent paper [ 12 ], a pan-sharpening approach, as shown in Figure 2 of [ 13 ], was proposed to demosaicing CFA 2.0. The demosaicing of the reduced resolution color image is done using LDI-NAT [ 16 ].…”

“…Recently, we systematically and thoroughly compared CFA 1.0 and CFA 2.0 under dark conditions [ 13 ]. We observed that CFA 2.0 indeed performed better under dark conditions.…”

Introductory Chapter: Recent Advances in Image Restoration

“…In [48], a further improved version by combining deep learning and pansharpening was proposed. In [49], a comparative study of the performance of CFA 1.0 and CFA 2.0 for low lighting images was carried out. It was found that CFA 2.0 has advantages over CFA 1.0 in low lighting conditions.…”

Untitled

“…It will be good to investigate a CFA that is robust to various lighting conditions. In the past, we have compared CFA 1.0 and CFA 2.0 for normal and low lighting images [20]. CFA 2.0 was observed to perform better than CFA 1.0 under dark environments.…”