Promoting the spatial resolution of hyperspectral sensors is expected to improve computer vision tasks. However, due to the physical limitations of imaging sensors, the hyperspectral image is often of low spatial resolution. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) hyperspectral image and a high resolution (HR) multispectral image of the same scene. The reconstruction of HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary is learned from the LR hyperspectral image. The sparse codes with respect to the learned dictionary are estimated from LR hyperspectral image and the corresponding HR multispectral image. To improve the accuracy, both spectral dictionary learning and sparse coefficients estimation exploit the spatial correlation of the HR hyperspectral image. Experiments show that the proposed method outperforms several state-of-art hyperspectral image super-resolution methods in objective quality metrics and visual performance.
This paper reports an experimental study of laser spot welding on stainless steel sheets. A pulsed Nd:YAG laser was used to weld the stainless steel specimen in the range of laser energy 0.6–1.2 J and incident angle 30–75° (the angle of the laser beam incident direction to the sheet surface). Metallography was applied to measure the cross-sectional size and shape of the welded spot. From the experimental results, it is found that as the laser energy increases, the penetration depth, bead length, and bead width of the welded spot increase. As the laser incident angle increases, the penetration depth and the bead width increase while the bead length decreases. The results illustrate that the shape and size of the welded spot depend not only on the laser energy, but also on the incident angle of laser beam. 相似文献
