Low-sampling-rate compressed ghost edge imaging via energy-descending ordered speckle patterns

Computational ghost imaging (CGI) for edge detection, particularly speckle-shifting ghost imaging (SSGI), faces a severe trade-off between sampling cost and edge quality. We propose energy-descending ordered compressed ghost edge imaging (EDO-CGEI), an adaptive edge detection method that reorders binary illumination patterns in descending order of energy. Simulations and experiments show that EDO-CGEI outperforms existing schemes at low sampling rates, achieving a satisfactory 256×256 edge image with a sampling rate of only 15%. This approach effectively pushes forward the trade-off between efficiency and clarity in ghost imaging edge detection under resource constraints.