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%.
Coherent fiber bundles (CFBs) can transmit light from one end to the other in identical order, which are the important components of fiber endoscopes. However, the tiny fiber tip limits the field of view, and the inherent honeycomb artifact degrades the image quality. Here, we proposed a high-resolution wide-field microendoscopic imaging method. Multi-frame CFB images with random displacement are continuously captured during the observation. Motion parameters can be automatically estimated by alternate optimization with high-resolution and wide-field image instead of image registration. The method can provide strong support for the diagnosis and treatment of major diseases such as cancer.
As the basis of virtual content creation, cameras are integral to augmented reality (AR) applications. However, the opaque nature of the camera's appearance can prevent it from being integrated into a transparent AR display. Here we introduced an integrated, compact, and flexible see-through camera, which enables crucial functionalities like eye gaze tracking and eye-position perspective photography, enhancing the immersive experience and interaction possibilities.
Fresnel zone aperture lensless imaging encodes incident light into a hologram-like pattern. However, the uncertain target distance leads to a blurry image with artifacts, which brings challenge to the estimation of object distances. We proposed an autofocusing method for FZA lensless imaging which is a superior solution for distance estimation. The image sharpness metrics are incorporated into the back-propagation process to obtain the focusing distance and reconstruct high-contrast and noise-free images. The Tamura gradient, nuclear norm of gradient and the gradient of the image are combined together to obtain a new evaluation metric. The acquisition accuracy is higher and the time is faster by using this new metric to obtain the focus distance. The proposed method paves the way to the micro sensors.
An end-to-end tumor diagnosis framework including resolution enhancement and tumor classification is proposed. The U-Net + EDSR network enables a significant improvement of PSNR and enhances the resolution beyond physical limitations. Moreover, the subsequent tumor discrimination can benefit from the enhancement. Multi-image as network input and advanced models like generative adversarial networks are expected to bring a further improvement for the imaging. Our proposed novel method first time realizes intraoperative lensless CFB imaging with high resolution in the near-field. The technique builds a bridge to many techniques like optical biopsies, multi-modal imaging, virtual staining, and computer-assisted disease diagnostics for neuron signal monitoring as well as neurosurgery.
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