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As compressive imaging can capture high-resolution images using low-resolution detectors, it has received extensive attention recently. Compared to Single-pixel Compressive imaging, block compressive imaging (BCI) can considerably reduce the observation and calculation time of the reconstruction process, therefore it can also reduce the speed of imaging. A common challenge in BCI implementation is system calibration. In this paper, we use system spread point function into object reconstruction process to solve this challenge. In our simulation works, a 64x64 object with block size 4x4 is used. 6 measurements are collected for each block. Orthogonal matching pursuit (OMP) algorithm is applied to reconstruction. Additionally, we setup an experiment to demonstrate BCI idea. The BCI experimental platform confirms that images at high spatial resolution can be successfully recovered from low-resolution sensor.
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