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Special Issue "Recent Advances in Color Filter Arrays and Demosaicing of Color and Multispectral Images"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Chiman Kwan
Website
Guest Editor
Signal Processing, Inc., Rockville, MD, USA
Interests: Electronic nose; image demosacing; speech processing; image processing; remote sensing; deep learning; fault-tolerant control; fault diagnostics and prognostics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Since the invention of the Kodak Bayer pattern, which is the most popular color filter array (CFA), many digital cameras have deployed CFAs. A Bayer CFA has even been used in the Mastcam imagers onboard the Mars rover Curiosity. In recent years, many variants of CFAs have been invented, including various RGBW CFAs, universal CFAs, etc. Associated with the development of CFAs, newer and powerful demosaicing algorithms have also been developed, including deep learning. Recently, new cameras with CFA 2.0 have been manufactured (BOBCAT IGV-B2320 v1.0, manufactured by IMPERX). The new cameras are suitable for both normal and low-light environments. This Special Issue focuses on new developments in digital cameras using various CFAs as well as demosaicing algorithms for color, multispectral, and hyperspectral imagers. The following topics are of interest:

  • New sensors/imagers that incorporate various CFAs;
  • Optimization of imagers/sensors that incorporate CFAs under diverse lighting conditions;
  • Special imagers for foggy, hazy, or turbulent environments;
  • New CFA patterns;
  • New demosaicing algorithms for color, multispectral, and hyperspectral images;
  • New joint denoising and demosaicing algorithms;
  • New inpainting algorithms with application to demosaicing;
  • New denoising algorithms for improving demosaicing in low lighting conditions.

Dr. Chiman Kwan
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • color filter array
  • demosaicing
  • denoising
  • deep learning
  • low-light conditions

Published Papers (2 papers)

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Research

Open AccessArticle
Demosaicing of CFA 3.0 with Applications to Low Lighting Images
Sensors 2020, 20(12), 3423; https://doi.org/10.3390/s20123423 - 17 Jun 2020
Cited by 1
Abstract
Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as [...] Read more.
Low lighting images usually contain Poisson noise, which is pixel amplitude-dependent. More panchromatic or white pixels in a color filter array (CFA) are believed to help the demosaicing performance in dark environments. In this paper, we first introduce a CFA pattern known as CFA 3.0 that has 75% white pixels, 12.5% green pixels, and 6.25% of red and blue pixels. We then present algorithms to demosaic this CFA, and demonstrate its performance for normal and low lighting images. In addition, a comparative study was performed to evaluate the demosaicing performance of three CFAs, namely the Bayer pattern (CFA 1.0), the Kodak CFA 2.0, and the proposed CFA 3.0. Using a clean Kodak dataset with 12 images, we emulated low lighting conditions by introducing Poisson noise into the clean images. In our experiments, normal and low lighting images were used. For the low lighting conditions, images with signal-to-noise (SNR) of 10 dBs and 20 dBs were studied. We observed that the demosaicing performance in low lighting conditions was improved when there are more white pixels. Moreover, denoising can further enhance the demosaicing performance for all CFAs. The most important finding is that CFA 3.0 performs better than CFA 1.0, but is slightly inferior to CFA 2.0, in low lighting images. Full article
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Open AccessArticle
Land Cover Change in the Central Region of the Lower Yangtze River Based on Landsat Imagery and the Google Earth Engine: A Case Study in Nanjing, China
Sensors 2020, 20(7), 2091; https://doi.org/10.3390/s20072091 - 08 Apr 2020
Cited by 2
Abstract
Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work [...] Read more.
Urbanization in China is progressing rapidly and continuously, especially in the newly developed metropolitan areas. The Google Earth Engine (GEE) is a powerful tool that can be used to efficiently investigate these changes using a large repository of available optical imagery. This work examined land-cover changes in the central region of the lower Yangtze River and exemplifies the application of GEE using the random forest classification algorithm on Landsat dense stacks spanning the 30 years from 1987 to 2017. Based on the obtained time-series land-cover classification results, the spatiotemporal land-use/cover changes were analyzed, as well as the main factors driving the changes in different land-cover categories. The results show that: (1) The obtained land datasets were reliable and highly accurate, with an overall accuracy ranging from 88% to 92%. (2) Over the past 30 years, built-up areas have continued to expand, increasing from 537.9 km2 to 1500.5 km2, and the total area occupied by built-up regions has expanded by 178.9% to occupy an additional 962.7 km2. The surface water area first decreased, then increased, and generally showed an increasing trend, expanding by 17.9%, with an area increase of approximately 131 km2. Barren areas accounted for 6.6% of the total area in the period 2015–2017, which was an increase of 94.8% relative to the period 1987–1989. The expansion of the built-up area was accompanied by an overall 25.6% (1305.7 km2) reduction in vegetation. (3) The complexity of the key factors driving the changes in the regional surface water extent was made apparent, mainly including the changes in runoff of the Yangtze River and the construction of various water conservancy projects. The effects of increasing the urban population and expanding industrial development were the main factors driving the expansion of urban built-up areas and the significant reduction in vegetation. The advantages and limitations arising from land-cover mapping by using the Google Earth Engine are also discussed. Full article
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