Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN

Yan, Yiqi; Zhang, Lei; Li, Jun; Wei, Wei; Zhang, Yanning

doi:10.48550/arxiv.1806.03575

Cited by 1 publication

(1 citation statement)

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“…The are works [26,2,4,32,1,10] which employ example-based spectral reconstruction on RGB images. Most common approaches utilize sparse dictionary learning and deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Towards Spectral Estimation from a Single RGB Image in the Wild

Kaya

Can

Timofte

2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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In contrast to the current literature, we address the problem of estimating the spectrum from a single common trichromatic RGB image obtained under unconstrained settings (e.g. unknown camera parameters, unknown scene radiance, unknown scene contents). For this we use a reference spectrum as provided by a hyperspectral image camera, and propose efficient deep learning solutions for sensitivity function estimation and spectral reconstruction from a single RGB image. We further expand the concept of spectral reconstruction such that to work for RGB images taken in the wild and propose a solution based on a convolutional network conditioned on the estimated sensitivity function. Besides the proposed solutions, we study also generic and sensitivity specialized models and discuss their limitations. We achieve state-of-the-art competitive results on the standard example-based spectral reconstruction benchmarks: ICVL, CAVE, NUS and NTIRE. Moreover, our experiments show that, for the first time, accurate spectral estimation from a single RGB image in the wild is within our reach. 1

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“…The are works [26,2,4,32,1,10] which employ example-based spectral reconstruction on RGB images. Most common approaches utilize sparse dictionary learning and deep learning methods.…”

Section: Introductionmentioning

confidence: 99%