Recently, a color filter array sensor with the white channel has been developed. This color filter array differs from the Bayer CFA, which is composed of red, green and blue channels. Since the white channel shows high sensitivity through broad spectral bands and a high light sensitivity, it presents many advantages. However, various color interpolation method for the Bayer CFA cannot be utilized for CFA pattern that contains the white channel directly. In this paper, a method for generating a quincuncial pattern is proposed for the CFA pattern. By generating an intermediate quincuncial pattern, various color interpolation algorithms can be applied to it. Experimental results are shown in comparison with the conventional method in terms of PSNR measurements.
Recently, color filter array patterns based on optimal design by reducing interchannel aliasing have been developed. Although these optimally subsampled patterns produce less degradation of resolution for the images in daylight about 6500 K, aliasing and grid effect become more prominent under the different lighting conditions. Moreover, the color interpolation (CI) for Bayer pattern cannot be utilized for these patterns. Since it is impossible to change coated patterns according to various conditions, CI algorithms have to compensate for light variations. In this letter, a method for generating intermediate quincuncial pattern is proposed for two patterns. Also, edge adaptive interpolation for the quincuncial pattern is carried out. Experimental results show that the proposed method reduces aliasing and grid effect.
In the denoising literature, research based on the nonlocal means (NLM) filter has been done and there have been many variations and improvements regarding weight function and parameter optimization. Here, a NLM filter with patch-based difference (PBD) refinement is presented. PBD refinement, which is the weighted average of the PBD values, is performed with respect to the difference images of all the locations in a refinement kernel. With refined and denoised PBD values, pattern adaptive smoothing threshold and noise suppressed NLM filter weights are calculated. Owing to the refinement of the PBD values, the patterns are divided into flat regions and texture regions by comparing the sorted values in the PBD domain to the threshold value including the noise standard deviation. Then, two different smoothing thresholds are utilized for each region denoising, respectively, and the NLM filter is applied finally. Experimental results of the proposed scheme are shown in comparison with several state-of-the-arts NLM based denoising methods.
Spatial-temporal filters have been widely used in video denoising module. The filters are commonly designed for
monochromatic image. However, most digital video cameras use a color filter array (CFA) to get color sequence. We
propose a recursive spatial-temporal filter using motion estimation (ME) and motion compensated prediction (MCP) for
CFA sequence. In the proposed ME method, we obtain candidate motion vectors from CFA sequence through
hypothetical luminance maps. With the estimated motion vectors, the accurate MCP is obtained from CFA sequence by
weighted averaging, which is determined by spatial-temporal LMMSE. Then, the temporal filter combines estimated
MCP and current pixel. This process is controlled by the motion detection value. After temporal filtering, the spatial
filter is applied to the filtered current frame as a post-processing. Experimental results show that the proposed method
achieves good denoising performance without motion blurring and acquires high visual quality.
Imaging sensors have physical limitations in spatial resolution, spectral resolution and dynamic range. Super-resolution
(SR) image processing technology is to overcome these physical limitations. For decades, numerous researches have
been conducted from theoretical points of view, and a variety of high-performance SR algorithms have been proposed.
However, there is little work on the implementation of real-world SR imaging system. We have implemented two types
of SR imaging systems. First, 9-eye system designed as a prototype is presented, and then 6-eye big system following the
prototype is announced and demonstrated. The proposed SR algorithms and a few conventional SR algorithms are
applied to both of the SR imaging systems. Problems and further study in SR imaging systems are constructed and
discussed through experimental results.