Physically Plausible Spectral Reconstruction

Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods—with the very best algorithms using deep learning—can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly—i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera’s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases

On the optimization of regression-based spectral reconstruction

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from RGB camera responses. Recently, the most common metric for evaluating the performance of SR algorithms is the Mean Relative Absolute Error (MRAE)—an ℓ 1 relative error (also known as percentage error). Unsurprisingly, the leading algorithms based on Deep Neural Networks (DNN) are trained and tested using the MRAE metric. In contrast, the much simpler regression-based methods (which actually can work tolerably well) are trained to optimize a generic Root Mean Square Error (RMSE) and then tested in MRAE. Another issue with the regression methods is—because in SR the linear systems are large and ill-posed—that they are necessarily solved using regularization. However, hitherto the regularization has been applied at a spectrum level, whereas in MRAE the errors are measured per wavelength (i.e., per spectral channel) and then averaged. The two aims of this paper are, first, to reformulate the simple regressions so that they minimize a relative error metric in training—we formulate both ℓ 2 and ℓ 1 relative error variants where the latter is MRAE—and, second, we adopt a per-channel regularization strategy. Together, our modifications to how the regressions are formulated and solved leads to up to a 14% increment in mean performance and up to 17% in worst-case performance (measured with MRAE). Importantly, our best result narrows the gap between the regression approaches and the leading DNN model to around 8% in mean accuracy

Color in scientific visualization: Perception and image-based data display

Visualization is the transformation of information into a visual display that enhances users understanding and interpretation of the data. This thesis project has investigated the use of color and human vision modeling for visualization of image-based scientific data. Two preliminary psychophysical experiments were first conducted on uniform color patches to analyze the perception and understanding of different color attributes, which provided psychophysical evidence and guidance for the choice of color space/attributes for color encoding. Perceptual color scales were then designed for univariate and bivariate image data display and their effectiveness was evaluated through three psychophysical experiments. Some general guidelines were derived for effective color scales design. Extending to high-dimensional data, two visualization techniques were developed for hyperspectral imagery. The first approach takes advantage of the underlying relationships between PCA/ICA of hyperspectral images and the human opponent color model, and maps the first three PCs or ICs to several opponent color spaces including CIELAB, HSV, YCbCr, and YUV. The gray world assumption was adopted to automatically set the mapping origins. The rendered images are well color balanced and can offer a first look capability or initial classification for a wide variety of spectral scenes. The second approach combines a true color image and a PCA image based on a biologically inspired visual attention model that simulates the center-surround structure of visual receptive fields as the difference between fine and coarse scales. The model was extended to take into account human contrast sensitivity and include high-level information such as the second order statistical structure in the form of local variance map, in addition to low-level features such as color, luminance, and orientation. It generates a topographic saliency map for both the true color image and the PCA image, a difference map is then derived and used as a mask to select interesting locations where the PCA image has more salient features than available in the visible bands. The resulting representations preserve consistent natural appearance of the scene, while the selected attentional locations may be analyzed by more advanced algorithms