{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T18:14:11Z","timestamp":1780424051358,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Color image demosaicking for the Bayer color filter array is an essential image processing operation for acquiring high-quality color images. Recently, residual interpolation (RI)-based algorithms have demonstrated superior demosaicking performance over conventional color difference interpolation-based algorithms. In this paper, we propose adaptive residual interpolation (ARI) that improves existing RI-based algorithms by adaptively combining two RI-based algorithms and selecting a suitable iteration number at each pixel. These are performed based on a unified criterion that evaluates the validity of an RI-based algorithm. Experimental comparisons using standard color image datasets demonstrate that ARI can improve existing RI-based algorithms by more than 0.6 dB in the color peak signal-to-noise ratio and can outperform state-of-the-art algorithms based on training images. We further extend ARI for a multispectral filter array, in which more than three spectral bands are arrayed, and demonstrate that ARI can achieve state-of-the-art performance also for the task of multispectral image demosaicking.<\/jats:p>","DOI":"10.3390\/s17122787","type":"journal-article","created":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T12:30:16Z","timestamp":1512131416000},"page":"2787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Adaptive Residual Interpolation for Color and Multispectral Image Demosaicking"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6733-3406","authenticated-orcid":false,"given":"Yusuke","family":"Monno","sequence":"first","affiliation":[{"name":"Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Kiku","sequence":"additional","affiliation":[{"name":"Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masayuki","family":"Tanaka","sequence":"additional","affiliation":[{"name":"Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan"},{"name":"Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Koto-ku, Tokyo 135-0064, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masatoshi","family":"Okutomi","sequence":"additional","affiliation":[{"name":"Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lukac, R. 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