Abstract:The Spectral Power Distributions (SPD) of outdoor light sources are not constant over time and atmospheric conditions, which causes the appearance variation of a scene and common natural illumination phenomena, such as twilight, shadow, and haze/fog. Calculating the SPD of outdoor light sources at different time (or zenith angles) and under different atmospheric conditions is of interest to physically-based vision. In this paper, for computer vision and its applications, we propose a feasible, simple, and effective SPD calculating method based on analyzing the transmittance functions of absorption and scattering along the path of solar radiation through the atmosphere in the visible spectrum. Compared with previous SPD calculation methods, our model has less parameters and is accurate enough to be directly applied in computer vision. It can be applied in computer vision tasks including spectral inverse calculation, lighting conversion, and shadowed image processing. The experimental results of the applications demonstrate that our calculation methods have practical values in computer vision. It establishes a bridge between image and physical environmental information, e.g., time, location, and weather conditions.
Hyperspectral images (HSIs) have high spectral resolution and low spatial resolution. HSI super-resolution (SR) can enhance the spatial information of the scene. Current SR methods have generally focused on the direct utilization of image structure priors, which are often modeled in global or local lower-order image space. The spatial and spectral hidden priors, which are accessible from higher-order space, cannot be taken advantage of when using these methods. To solve this problem, we propose a higher-order Hankel space-based hyperspectral image-multispectral image (HSI-MSI) fusion method in this paper. In this method, the higher-order tensor represented in the Hankel space increases the HSI data redundancy, and the hidden relationships are revealed by the nonconvex penalized Kronecker-basis-representation-based tensor sparsity measure (KBR). Weighted 3D total variation (W3DTV) is further applied to maintain the local smoothness in the image structure, and an efficient algorithm is derived under the alternating direction method of multipliers (ADMM) framework. Extensive experiments on three commonly used public HSI datasets validate the superiority of the proposed method compared with current state-of-the-art SR approaches in image detail reconstruction and spectral information restoration.
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