The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Image demosaicing: a systematic survey
Abstract
Image demosaicing is a problem of interpolating full-resolution color images from so-called color-filter-array (CFA) samples. Among various CFA patterns, Bayer pattern has been the most popular choice and demosaicing of Bayer pattern has attracted renewed interest in recent years partially due to the increased availability of source codes/executables in response to the principle of "reproducible research". In this article, we provide a systematic survey of over seventy published works in this field since 1999 (complementary to previous reviews22, 67). Our review attempts to address important issues to demosaicing and identify fundamental differences among competing approaches. Our findings suggest most existing works belong to the class of sequential demosaicing - i.e., luminance channel is interpolated first and then chrominance channels are reconstructed based on recovered luminance information. We report our comparative study results with a collection of eleven competing algorithms whose source codes or executables are provided by the authors. Our comparison is performed on two data sets: Kodak PhotoCD (popular choice) and IMAX high-quality images (more challenging). While most existing demosaicing algorithms achieve good performance on the Kodak data set, their performance on the IMAX one (images with varying-hue and high-saturation edges) degrades significantly. Such observation suggests the importance of properly addressing the issue of mismatch between assumed model and observation data in demosaicing, which calls for further investigation on issues such as model validation, test data selection and performance evaluation.
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Cites
Multirate Systems and Filter Banks
Vaidyanathan, P. P. (1992) · CaltechAUTHORS (California Institute of Technology) · Cited by 5,457
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,388
New edge-directed interpolation
Li, X., & Orchard, M. T. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.951537 · Cited by 2,023
Self-citationBrill, M. H. (1998) · Color Research & Application · doi:10.1002/(sici)1520-6378(199808)23:4<248::aid-col9>3.0.co;2-o · Cited by 1,367
Color Image Processing and Applications
Plataniotis, K. N., & Venetsanopoulos, A. N. (2000) · Digital signal processing · doi:10.1007/978-3-662-04186-4 · Cited by 1,157
A spatial extension of CIELAB for digital color‐image reproduction
Zhang, X., & Wandell, B. A. (1997) · Journal of the Society for Information Display · doi:10.1889/1.1985127 · Cited by 733
Color plane interpolation using alternating projections
Gunturk, B. K., Altunbasak, Y., & Mersereau, R. M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801121 · Cited by 621
Self-citationDemosaicking: color filter array interpolation
Gunturk, B. K., Glotzbach, J., Altunbasak, Y., Schafer, R. W., & Mersereau, R. M. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1407714 · Cited by 582
Self-citationDemosaicing: image reconstruction from color CCD samples
Kimmel, R. (1999) · IEEE Transactions on Image Processing · doi:10.1109/83.784434 · Cited by 472
Adaptive homogeneity-directed demosaicing algorithm
Hirakawa, K., & Parks, T. W. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.838691 · Cited by 439
Cited by
Deep joint demosaicking and denoising
Gharbi, M., Chaurasia, G., Paris, S., & Durand, F. (2016) · ACM Transactions on Graphics · doi:10.1145/2980179.2982399 · Cited by 473
ISP Meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing
Santos, C. F. G. D., Arrais, R. R., Silva, J. V. S. D., Silva, M. H. M. D., Neto, W. B. G. D. A., Lopes, L. T., Bileki, G. A., Lima, I. O., Rondon, L. B., Souza, B. M. D., et al. (2025) · ACM Computing Surveys · doi:10.1145/3708516 · Cited by 9
“There are some surveys about individual steps of an ISP, such as demosaicing [68] and denoising [25], but not other works about end-to-end ISP via Deep Learning.”
Metadata
- Authors
- Li, X., Gunturk, B., & Zhang, L.
- Venue
- SPIE Proceedings
- Publisher
- SPIE
- Published
- 2008-01-27
- Volume
- 6822
- Pages
- 68221J
- ISSN
- 0277-786X (ISSN-L)
- OpenAlex
- W2035192779
- Cited by
- 419
- References
- 24
- FWCI
- 12.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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