Name
Abstract | ||
|---|---|---|
We present an efficient method for the reconstruction of multispectral information from RGB images, as part of the NTIRE 2022 Spectral Reconstruction Challenge. Given an input image, our method determines a global RGB-to-spectral linear transformation matrix, based on a search through optimal matrices from training images that share low-level features with the input. The resulting spectral signatures are then adjusted by a global scaling factor, determined through a lightweight SqueezeNet-inspired neural network. By combining the efficiency of linear transformation matrices with the data-driven effectiveness of convolutional neural networks, we are able to achieve superior performance than winners of the previous editions of the challenge. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPRW56347.2022.00122 | IEEE Conference on Computer Vision and Pattern Recognition |
DocType | Volume | Issue |
Conference | 2022 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mirko Agarla | 1 | 0 | 0.68 |
| Simone Bianco | 2 | 226 | 24.48 |
| Marco Buzzelli | 3 | 0 | 1.01 |
| Luigi Celona | 4 | 0 | 0.34 |
| Raimondo Schettini | 5 | 1476 | 154.06 |