Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 25, Issue 3, Pages 363-367Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2017.2776167
Keywords
Multispectral image; sparse coding; spectral reconstruction
Categories
Funding
- National Natural Science Foundation of China [61602268, 61603202, 61571247]
- K. C. Wong Magna Fund in Ningbo University
Ask authors/readers for more resources
Training-based spectral reconstruction is an efficient, inexpensive technique to recover spectral images from the RGB images captured by trichromatic cameras. Existing methods handle training samples individually without any consideration of local spatial and spectral correlations between samples, which results in high metamerism and inaccurate reconstruction. In this letter, we exploit for the first time the concept of spectral image reconstruction from RGB images with both chromatic and texture priors. We reduce redundancy of the sample set by applying a volume maximization based selection strategy. Taking advantage of the local linearity and sparsity of spectra in dictionary learning, we propose a locally linear embedded sparse reconstruction method taking into account both RGB values of pixels and the features of patch texture. Experimental results show that our method is significantly more accurate than the state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available