Wavefront coding image reconstruction via physical prior and frequency attention

Wavefront coding (WFC) is an effective technique for extending the depth-of-field of imaging systems, including optical encoding and digital decoding. We applied physical prior information and frequency domain model to the wavefront decoding, proposing a reconstruction method by a generative model. Specifically, we rebuild the baseline inspired by the transformer and propose three modules, including the point spread function (PSF) attention layer, multi-feature fusion block, and frequency domain self-attention block. These models are used for end-to-end learning to extract PSF feature information, fuse it into the image features, and further re-normalize the image feature information, respectively. To verify the validity, in the encoding part, we use the genetic algorithm to design a phase mask in a large field-of-view fluorescence microscope system to generate the encoded images. And the experimental results after wavefront decoding show that our method effectively reduces noise, artifacts, and blur. Therefore, we provide a deep-learning wavefront decoding model, which improves reconstruction image quality while considering the large depth-of-field (DOF) of a large field-of-view system, with good potential in detecting digital polymerase chain reaction (dPCR) and biological images.

Increasing aperture and depth of field simultaneously with wavefront coding technology

We present a method that simultaneously increases the aperture and depth of field (DOF). A novel, to the best of our knowledge, method called lens-combined wavefront coding (WFC) is proposed for optical design. By rationally balancing rather than minimizing the aberrations, the DOF enhances instead of reduces with expansion of the aperture size. Two optical systems by traditional design and lens-combined WFC are designed to demonstrate our concept. Experiments are conducted using the manufactured lenses to show the extension of the DOF both in the laboratory and the real scene. A spatially invariant deconvolution algorithm is exploited to further suppress the aberrations regarding the field of view. The results show that the aperture and the DOF can be successfully enhanced at the same time.

Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution

Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

Test of the Practicality and Feasibility of EDoF-Empowered Image Sensors for Long-Range Biometrics

For many practical applications of image sensors, how to extend the depth-of-field (DoF) is an important research topic; if successfully implemented, it could be beneficial in various applications, from photography to biometrics. In this work, we want to examine the feasibility and practicability of a well-known “extended DoF” (EDoF) technique, or “wavefront coding,” by building real-time long-range iris recognition and performing large-scale iris recognition. The key to the success of long-range iris recognition includes long DoF and image quality invariance toward various object distance, which is strict and harsh enough to test the practicality and feasibility of EDoF-empowered image sensors. Besides image sensor modification, we also explored the possibility of varying enrollment/testing pairs. With 512 iris images from 32 Asian people as the database, 400-mm focal length and F/6.3 optics over 3 m working distance, our results prove that a sophisticated coding design scheme plus homogeneous enrollment/testing setups can effectively overcome the blurring caused by phase modulation and omit Wiener-based restoration. In our experiments, which are based on 3328 iris images in total, the EDoF factor can achieve a result 3.71 times better than the original system without a loss of recognition accuracy.

IRIS RECOGNITION OF DEFOCUSED IMAGES FOR MOBILE PHONES

In this paper, we introduce a novel iris recognition approach for mobile phones, which takes into account imaging noise arising from image capture outside the depth of field (DOF) of cameras. Unlike existing approaches that rely on special hardware to extend the DOF or computationally expensive algorithms to restore the defocused images prior to recognition, the proposed method performs recognition on the defocused images based on the stable bits in the iris code representation that are robust to imaging noise. To the best of our knowledge, our work is the first to investigate the characteristics of iris features for varying degree of image defocus when the images are captured outside the DOF of cameras. Based on our findings, we present a method to determine the stable bits of an enrolled image. When compared to iris recognition of defocused images that relies on the entire code representation, the proposed recognition method increases the inter-class variability while reducing the intra-class variability of the samples considered. This leads to smaller intersections between the intra-class and inter-class distance distributions, which results in higher recognition performance. Experimental results based on over 15,000 images show that the proposed method achieves an average recognition performance gain of about two times. It is envisioned that the proposed method can be incorporated as part of a multi-biometric system for mobile phones due to its lightweight computational requirements, which is well suited for power sensitive solutions.