The coded-aperture imaging technique needs only a thin coded mask to encode image, which enable to build an ultra-thin imaging system. However, the limited dynamic range of the image sensor and the diffraction effect degrades the reconstructed images quality. Here, we proposed an integrated ultra-thin coded aperture lensless camera. We take Fresnel zone plates as coded mask, then the incident light could be encoded into a hologram-like pattern, and the image can be holographic reconstruction. A deep neural network is also trained for rapid and high-quality reconstruction. To improve the dynamic range, differential-enhancement method is used by capture two complementary encoding images. The proposed integrated ultra-thin camera is expected to be applied in unconscious payment, identification and authentication, autonomous cars, etc.
Fresnel zone aperture (FZA) lensless imaging encode the incident light into hologram-like pattern, so that the scene image can be refocused by back propagation method. However, the inherent twin image and inaccuracy focusing distance degrade the imaging quality. This brings difficulties for the target classification and recognition applications. We proposed a high-quality reconstruction and autofocusing method for FZA lensless imaging. By investigating the image sharpness metrics on the back propagation images, the accuracy focusing distance could be estimated. Total variation regularization based alternating direction method of multipliers algorithm is proposed to suppress the twin image existing in the back propagation reconstruction. Experimental results show that the proposed method can significantly improve the target recognition rate from 4.06% to 90.00%.
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