In this paper, we propose a reflective augmented reality (AR) display system based on integral imaging (II) using a mirror-based pinhole array (MBPA). The MBPA, obtained by punching pinholes on a mirror, functions as a three-dimensional (3D) imaging device, as well as an image combiner. The pinhole array of MBPA can realize a pinhole array-based II display, while the mirror of MBPA can image the real objects, so as to combine the images of the real objects with the reconstructed 3D images. The structure of the proposed reflective AR display is very simple, and only a projection system or a two-dimensional display screen is needed to combine with the MBPA. In our experiment, a 25cm × 14cm sized AR display was built up, a combination of a 3D virtual image and a real 3D object was presented by the proposed AR 3D display. The proposed device could realize an AR display of large size due to its compact form factor and low weight.

Under the framework of integral imaging, a high-precision 3D salient object detection and high-quality texture features reconstruction method is proposed by using the element to element-transformer and generative adversarial network (E2E-TransGAN). The proposed method fully exploits the global salient clues of the element image array to establish cross-regional element image dependency, thereby the accuracy of the salient prediction improved. Meanwhile, our proposed E2E-TransGAN algorithm stratifies the binary salient object area and the grayscale salient object edge, effectively addressing salient region prediction errors, obvious background noise, and color loss. The experimental results show that the detection accuracy of the proposed method improves to an average of 90% across various evaluation metrics. Compared with other methods, the proposed method can reconstruct 3D images with texture features, which have high values of PSNR and SSIM, at 30 and 0.95 on average, respectively. We have experimentally verified that the proposed method achieves precise 3D object salient detection and restores the detailed textures and depth information of 3D salient objects. The proposed method provides strong support for efficient and accurate medical image analysis and simplifies subsequent computer vision tasks.

Potent usage of the multi-scale light field information for salient object detection (SOD) is the essential requirement of three-dimensional (3D) SOD. On this basis, a light field 3D-SOD scheme is proposed that employs the pixel mapping algorithm to achieve a more distinct representation of spatial and angular information in the four-dimensional (4D) light field, collaboratively mining the global saliency cues via the co-salient object detection (CoSOD) network. Compared with the previous method, our scheme filters out most of the noise by thoroughly leveraging the global dependence of the 4D light field, offering significant enhancements in saliency extraction performance and efficiency. Additionally, the 3D reconstruction results demonstrate the integral retention of the spatial and angular information of the original light field.

The development of optical and photonic applications using soft-matter droplets holds great scientific and application importance. The machining of droplet structures is expected to drive breakthroughs in advancing frontier applications. This review highlights recent advancements in micro–nanofabrication techniques for soft-matter droplets, encompassing microfluidics, laser injection, and microfluidic 3D printing. The principles, advantages, and weaknesses of these technologies are thoroughly discussed. The review introduces the utilization of a phase separation strategy in microfluidics to assemble complex emulsion droplets and control droplet geometries by adjusting interfacial tension. Additionally, laser injection can take full advantage of the self-assembly properties of soft matter to control the spontaneous organization of internal substructures within droplets, thus providing the possibility of high-precision customized assembly of droplets. Microfluidic 3D printing demonstrates a 3D printing-based method for machining droplet structures. Its programmable nature holds promise for developing device-level applications utilizing droplet arrays. Finally, the review presents novel applications of soft-matter droplets in optics and photonics. The integration of processing concepts from microfluidics, laser micro–nano-machining, and 3D printing into droplet processing, combined with the self-assembly properties of soft materials, may offer novel opportunities for processing and application development.

The advent of deep learning has brought a revolutionary transformation to image denoising techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised methods in real-world scenarios remains formidable, necessitating the exploration of more practical self-supervised image denoising. This paper focuses on self-supervised image denoising methods that offer effective solutions to address this challenge. Our comprehensive review thoroughly analyzes the latest advancements in self-supervised image denoising approaches, categorizing them into three distinct classes: General methods, Blind Spot Network (BSN)-based methods, and Transformer-based methods. For each class, we provide a concise theoretical analysis along with their practical applications. To assess the effectiveness of these methods, we present both quantitative and qualitative experimental results on various datasets, utilizing classical algorithms as benchmarks. Additionally, we critically discuss the current limitations of these methods and propose promising directions for future research. By offering a detailed overview of recent developments in self-supervised image denoising, this review serves as an invaluable resource for researchers and practitioners in the field, facilitating a deeper understanding of this emerging domain and inspiring further advancements.