Demosaicing enhancement using pixel-level fusion

Demosaicing of Bayer and CFA 2.0 Patterns for Low Lighting Images

“…This method was investigated in Bell et al's paper [2]; • Lu and Tan Interpolation (LT): This is a frequency domain approach [26]; • Adaptive Frequency Domain (AFD): This is a frequency domain approach from Dubois [27]. The algorithm can also be used for other mosaicing patterns; • Alternate Projection (AP): This is the algorithm from Gunturk et al [28]; • Primary-Consistent Soft-Decision (PCSD): This is Wu and Zhang's algorithm from [29]; • Alpha Trimmed Mean Filtering (ATMF): This method is from [30,31]. At each pixel location, we demosaic pixels from seven methods; the largest and smallest pixels are removed and the mean of the remaining pixels are used; • Demosaicnet (Demonet): In [32], a feed-forward network architecture was proposed for demosaicing.…”

“…Additionally, some challenging images were searched to further enhance the training model. Details can be found in [32]; • Fusion using three best (F3) [30]: The mean of pixels from demosaiced images of the three best individual methods were used; • Bilinear: Bilinear interpolation is the simplest algorithm that uses the nearest neighbors for interpolation; • Sequential Energy Minimization (SEM) [33]: A deep learning approach based on sequential energy minimization was proposed in [33]. The performance was reasonable, except that the computation takes a long time due to sequential optimization; • Exploitation of Color Correlation (ECC) [34]: The authors of [34] proposed a scheme that exploits the correlation between different color channels much more effectively than some of the existing algorithm; • Minimized-Laplacian Residual Interpolation (MLRI) [35]: This is a residual interpolation (RI)-based algorithm based on a minimized-Laplacian version; • Adaptive Residual Interpolation (ARI) [36]: ARI adaptively combines RI and MLRI at each pixel, and adaptively selects a suitable iteration number for each pixel, instead of using a common iteration number for all of the pixels; • Directional Difference Regression (DDR) [37]: DDR obtains the regression models using directional color differences of the training images.…”

“…It should be noted that F3 and ATMF are both pixel-level fusion methods. Details can be found in [30]. In addition to the above mentioned algorithms for CFA 2.0, we also applied least-squares luma-chroma demultiplexing (LSLCD) over [53] in our experiments.…”

“…We also have two fusion based algorithms known as F3 and ATMF, which were used in our earlier studies [16,30,52]. F3 fuses the three best performing algorithms and ATMF fuses seven high-performing algorithms.…”

“…We have seen good performance improvement.Figure 4. A combination of deep learning and pansharpening approach to demosaicing CFA 2.0.In addition to the above mentioned algorithms for CFA 2.0, we also applied least-squares lumachroma demultiplexing (LSLCD) over [53] in our experiments.We also have two fusion based algorithms known as F3 and ATMF, which were used in our earlier studies [16,30,52]. F3 fuses the three best performing algorithms and ATMF fuses seven highperforming algorithms.It should be noted that algorithms for CFA 2.0 are much fewer than those of CFA 1.0.…”