Method of discontinuous optical glass optimization based on aberration correction in optical design.

Optical glass selection is an important research object in optical design, which is an important way to aberration correction. However, these methods to our knowledge either do not correct aberration well or consume too much time. To efficiently solve the apochromatic problems in optical design, this paper presents what we believe to be a novel automatic optimization method for discontinuous optical glass based on auto glass selection (AGS).

Programmed cell death-related ABI1 is a critical mediator of abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by localized dilation of the abdominal aorta, posing a significant risk of rupture and fatal hemorrhage. While surgical and endovascular repair techniques have advanced, the underlying mechanisms driving AAA development remain unclear, hindering the development of effective preventive and therapeutic strategies. Using bioinformatics analysis of publicly available data sets, the study identified a strong correlation between cell death (CD) score and different types of programmed cell death scores in AAA samples.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed.

Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes.

Dark2Light: multi-stage progressive learning model for low-light image enhancement.

Due to severe noise and extremely low illuminance, restoring from low-light images to normal-light images remains challenging. Unpredictable noise can tangle the weak signals, making it difficult for models to learn signals from low-light images, while simply restoring the illumination can lead to noise amplification. To address this dilemma, we propose a multi-stage model that can progressively restore normal-light images from low-light images, namely Dark2Light.

Design of an optimized Alvarez lens based on the fifth-order polynomial combination.

This paper proposes an optimized design of the Alvarez lens by utilizing a combination of three fifth-order - polynomials. It can effectively minimize the curvature of the lens surface to meet the manufacturing requirements. The phase modulation function and aberration of the proposed lens are evaluated by using first-order optical analysis.

ASF-Transformer: neutralizing the impact of atmospheric turbulence on optical imaging through alternating learning in the spatial and frequency domains.

Atmospheric turbulence, a pervasive and complex physical phenomenon, challenges optical imaging across various applications. This paper presents the Alternating Spatial-Frequency (ASF)-Transformer, a learning-based method for neutralizing the impact of atmospheric turbulence on optical imaging. Drawing inspiration from split-step propagation and correlated imaging principles, we propose the Alternating Learning in Spatial and Frequency domains (LASF) mechanism.

Image restoration for optical zooming system based on Alvarez lenses.

Alvarez lenses are known for their ability to achieve a broad range of optical power adjustment by utilizing complementary freeform surfaces. However, these lenses suffer from optical aberrations, which restrict their potential applications. To address this issue, we propose a field of view (FOV) attention image restoration model for continuous zooming.

Direct distortion prediction method for AR-HUD dynamic distortion correction.

Dynamic distortion is one of the most critical factors affecting the experience of automotive augmented reality head-up displays (AR-HUDs). A wide range of views and the extensive display area result in extraordinarily complex distortions. Existing methods based on the neural network first obtain distorted images and then get the predistorted data for training mostly.

Nighttime Image Dehazing by Render.

Nighttime image dehazing presents unique challenges due to the unevenly distributed haze caused by the color change of artificial light sources. This results in multiple interferences, including atmospheric light, glow, and direct light, which make the complex scattering haze interference difficult to accurately distinguish and remove. Additionally, obtaining pairs of high-definition data for fog removal at night is a difficult task.

Snapshot hyperspectral imaging based on equalization designed DOE.

Hyperspectral imaging attempts to determine distinctive information in spatial and spectral domain of a target. Over the past few years, hyperspectral imaging systems have developed towards lighter and faster. In phase-coded hyperspectral imaging systems, a better coding aperture design can improve the spectral accuracy relatively.

Elimination of varying chromatic aberrations based on diffractive optics.

In telescopic systems consisting of Alvarez lenses, chromatic aberrations vary with the magnifications and the fields of view. Computational imaging has developed rapidly in recent years, therefore we propose a method of optimizing the DOE and the post-processing neural network in 2 stages for achromatic aberrations. We apply the iterative algorithm and the gradient descent method to optimize the DOE, respectively, and then adopt U-Net to further optimize the results.

Deep learning-based image reconstruction for photonic integrated interferometric imaging.

Photonic integrated interferometric imaging (PIII) is an emerging technique that uses far-field spatial coherence measurements to extract intensity information from a source to form an image. At present, low sampling rate and noise disturbance are the main factors hindering the development of this technology. This paper implements a deep learning-based method to improve image quality.

Omnidirectional, thin metasurface exhibiting selective absorption for un-polarized broadband incidence.

Thin devices with large areas have strong and omnidirectional absorption over a wide bandwidth and are in demand for applications such as energy harvesting, structural color, and vehicle LiDAR (laser detection and ranging). Despite persistent efforts in the design and fabrication of such devices, the simultaneous realization of all these desired properties remains a challenge. In this study, a 190-nm-thick metasurface with an area of 3 cm2, incorporating dielectric cylinder arrays, a chromium layer, a silicon nitride (SiNx) layer, and an aluminum layer is theoretically and experimentally demonstrated.

Fast method for designing broadband achromatic diffractive optical elements.

Diffractive optical elements play a crucial role in the miniaturization of the optical systems, especially in correcting achromatic aberration. Considering the rapidity and validity of the design method, we propose a fast method for designing broadband achromatic diffractive optical elements. Based on the direct binary search algorithm, some improvements have been made including the selection of the initial height map to mitigate the uncertainty, the reduction of the variations to accelerate the optimization and the increase of sampling rate to deal with the large operation bandwidth.

Non-blind optical degradation correction via frequency self-adaptive and finetune tactics.

In mobile photography applications, limited volume constraints the diversity of optical design. In addition to the narrow space, the deviations introduced in mass production cause random bias to the real camera. In consequence, these factors introduce spatially varying aberration and stochastic degradation into the physical formation of an image.

Computational Optics for Mobile Terminals in Mass Production.

Correcting the optical aberrations and the manufacturing deviations of cameras is a challenging task. Due to the limitation on volume and the demand for mass production, existing mobile terminals cannot rectify optical degradation. In this work, we systematically construct the perturbed lens system model to illustrate the relationship between the deviated system parameters and the spatial frequency response (SFR) measured from photographs.

Image restoration for real-world under-display imaging.

Under-display imaging technique was recently proposed to enlarge the screen-to-body ratio for full-screen devices. However, existing image restoration algorithms have difficulty generalizing to real-world under-display (UD) images, especially to images containing strong light sources. To address this issue, we propose a novel method for building a synthetic dataset (CalibPSF dataset) and introduce a two-stage neural network to solve the under-display imaging degradation problem.

Deep denoiser prior based deep analytic network for lensless image restoration.

Mask based lensless imagers have huge application prospects due to their ultra-thin body. However, the visual perception of the restored images is poor due to the ill conditioned nature of the system. In this work, we proposed a deep analytic network by imitating the traditional optimization process as an end-to-end network.

Research on Diagnostic Information of Smart Medical Care Based on Big Data.

The medical field has gradually become intelligent, and information and the research of intelligent medical diagnosis information have received extensive attention in the medical field. In response to this situation, this paper proposes a Hadoop-based medical big data processing system. The system first realized the ETL module based on Sqoop and the transmission function of unstructured data and then realized the distributed storage management function based on HDFS.

Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA.

In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network.

Rotated rectangular aperture imaging through multi-frame blind deconvolution with Hyper-Laplacian priors.

Rotated rectangular aperture imaging has many advantages in large aperture telephoto systems due to its lower cost and lower complexity. This technology makes it possible to build super large aperture telescopes. In this paper, we combine the ideas of deblurring with rotated rectangular aperture imaging and propose an image synthesis algorithm based on multi-frame deconvolution.

Lensless cameras using a mask based on almost perfect sequence through deep learning.

Mask-based lensless imaging cameras have many applications due to their smaller volumes and lower costs. However, due to the ill-nature of the inverse problem, the reconstructed images have low resolution and poor quality. In this article, we use a mask based on almost perfect sequence which has an excellent autocorrelation property for lensless imaging and propose a Learned Analytic solution Net for image reconstruction under the framework of unrolled optimization.

Image Deblurring Utilizing Inertial Sensors and a Short-Long-Short Exposure Strategy.

Image blur caused by camera movement is common in long-exposure photography. A recent approach to address image blur is to record camera motion via inertial sensors in imaging equipment such as smartphones and single-lens reflex (SLR) cameras. However, because of device performance limitations, directly estimating a blur kernel from sensor data is infeasible.