Hyperspectral Image Super-Resolution Reconstruction Method Based on Spectral Space Combination and Gradient Domain Loss
A hyperspectral image, gradient domain technology, applied in the field of hyperspectral image super-resolution reconstruction, can solve the problems of inability to capture hyperspectral images, inability to balance time complexity and reconstruction quality, poor reconstruction quality, etc. Reconstruction effect, improvement of reconstruction effect, effect of accelerating convergence speed
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[0083]1. Experimental data
[0084]The CAVE data set contains 32 hyperspectral data from different objects. Each object has 31 photos illuminated by different wavelengths. Each image is 512x512 in size and the wavelength range is 400nm-700nm (each wavelength interval is 10nm). The Harvard data set contains 50 real-world outdoor / indoor data under sunlight and 27 data under artificial synthetic light. The size of each data block is 1392x1040x31, and these 31 bands are evenly distributed between 420nm-720nm. For each Harvard sample, this experiment will cut it into a size of 1024x1024x31 to facilitate calculation.
[0085]In order to facilitate the comparison of the quality of reconstructed images, this experiment uses the average value of the wavelength range of 400nm-500nm (or 420nm-520nm) as the B channel of the color image, and the average value of 500nm-600nm (or 520nm-620nm) as the color image G channels, the average value of 600nm-700nm (or 620nm-720nm) is used as the R channel of the...
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