Journal of The Optical Society of America A-optics Image Science and Vision最新文献

A dual-resonance-peak photonic crystal fiber-surface plasmon resonance (PCF-SPR) refractive index (RI) sensor is designed for different wavelength ranges. The first resonance peak of the sensor is distributed in the wavelength range of 700-2350 nm, while the second peak is distributed in the range of 2350-5550 nm. In addition to detecting analytes using the full spectrum of constraint losses (CLs), it is also possible to use a single resonance peak to achieve the detection of analytes. By systematically optimizing the nanowire diameter, the diameter of the inner and outer layer air hole, the width of the groove, the polishing depth, and the distance from the outer layer air hole to the fiber core, the optimal structure of the sensor is finally determined. In this study, the sensor was studied by numerical analysis, and the characteristics of the sensor were evaluated by wavelength detection technology. The results show that within the RI range of 1.24-1.37, the sensor has a maximum wavelength sensitivity (WS) of 54700 nm/RIU for detecting the RI of analytes. Within the above refractive index range, the regression coefficient R ² of the dual-peak-resonance wavelength is 0.99993, ensuring the accuracy of the estimated resonance wavelength of the sensor. In addition, the sensor can also use dual-peak-shift sensitivity (DPSS) to detect the refractive index, which is a relatively new sensing technology. The maximum DPSS of the sensor is 95300 nm/RIU. Due to its high sensitivity and unique dual-peak characteristics, this sensor has wide application prospects in medical diagnosis, environmental monitoring, food safety, and other fields.

In recent years, sub-diffraction focusing has received substantial attention due to its versatility. However, achieving a flexible sub-diffraction focusing in the far field remains stimulating. Existing techniques either require complex fabrication facilities or are limited to the short focal length and high numerical aperture (NA) of the imaging system. Here, we introduce an optimization method for sub-diffraction focusing of a circularly polarized beam in the far field with a lens of large focal length. A cost-effective dielectric phase plate serves the purpose. By employing a phase plate composed of a thin layer of dielectric S i ₃ N ₄, the phase of the propagating beam is modulated in the beam's cross-section, which is divided into two regions of the opposite phase by the plate. A sub-diffraction focusing is achieved for a proper tunning between the two regions. In addition to sub-diffraction focusing, the phase plate is also capable of shaping the focus into a doughnut-shaped and a flat-top profile in the far field. This design provides a simple solution for sub-diffraction focusing and focus shaping that will find potential applications in optical imaging, optical trapping, and material processing.

In image processing and color science, colors are often specified by their luminance and chromaticity (such as Y x y). Chromaticities are color ratios, which can be difficult to compute reliably due to noise, when the tristimulus values are small, e.g., for dark colors. A detailed statistical analysis of ratio distributions shows that below a certain signal/noise ratio, the computed color ratios are very noisy and often wrong. This contrasts with human vision, where a given chromaticity viewed at high luminance will appear to the viewer as having a distinct color, but when that same chromaticity is viewed at low luminance, it will be seen as dark and almost hue-less. Therefore, dark color processing can take advantage of the perceptual characteristics to avoid producing excessive color noise and unnatural colors. In this study, we perform a detailed analysis of ratio distributions and propose a method to handle dark colors in image processing, using a logarithmic-like transformation (called plog) that maps dark colors to reduced excitation purity. A color ratio 0/0 is mapped to 1 (as the neutral). The plog transformation removes the singularity of the logarithmic transformation and allows us to estimate and process the ratios of dark colors in a manner consistent with human color perception without increasing color noise. It also offers the additional benefit of reducing the dynamic range of dark colors for tone reproduction.

As we all know, suppressing noise while maintaining detailed structure has been a challenging problem in the field of image enhancement, especially for color retinal images. In this paper, a dual-channel lightweight GAN named dilated shuffle generative adversarial network (DS-GAN) is proposed to solve the above problems. The lightweight generator consists of the RB branch used in the red-blue channels and the GN branch used in the green channel. The branches are then integrated with a cat function to generate enhanced images. The RB branch cascades six identical RB-enhanced modules and adds skip connections. The structure of the GN branch is similar to that of the RB branch. The generator simultaneously leverages the local context extraction capability of the normal convolution and the global information extraction capability of the dilated convolution. In addition, it facilitates the fusion and communication of feature information between channels through channel shuffle. Additionally, we utilize the lightweight image classification model ShuffleNetV2 as a discriminator to distinguish between enhanced images and corresponding labels. We also constructed a dataset for color retinal image enhancement by using traditional methods and a hybrid loss function by combining the MS-SSIM and perceptual loss for training the generator. With the proposed dataset and loss function, we train the DS-GAN successfully. We test our method on four publicly available datasets (Messidor, DIARETDB0, DRIVE, and FIRE) and a clinic dataset from the Tianjin Eye Hospital (China), and compare it with six existing image enhancement methods. The results show that the proposed method can simultaneously suppress noise, preserve structure, and enhance contrast in color retinal image enhancement. It gets better results than the compared methods in all cases. Furthermore, the model has fewer parameters, which provides the possibility of real-time image enhancement for portable devices.

Disorders affecting the retina pose a considerable risk to human vision, with an array of factors including aging, diabetes, hypertension, obesity, ocular trauma, and tobacco use exacerbating this issue in contemporary times. Optical coherence tomography (OCT) is a rapidly developing imaging modality that is capable of identifying early signs of vascular, ocular, and central nervous system abnormalities. OCT can diagnose retinal diseases through image classification, but quantifying the laceration area requires image segmentation. To overcome this obstacle, we have developed an innovative deep learning framework that can perform both tasks simultaneously. The suggested framework employs a parallel mask-guided convolutional neural network (PM-CNN) for the classification of OCT B-scans and a grade activation map (GAM) output from the PM-CNN to help a V-Net network (GAM V-Net) to segment retinal lacerations. The guiding mask for the PM-CNN is obtained from the auxiliary segmentation job. The effectiveness of the dual framework was evaluated using a combined dataset that encompassed four publicly accessible datasets along with an additional real-time dataset. This compilation included 11 categories of retinal diseases. The four publicly available datasets provided a robust foundation for the validation of the dual framework, while the real-time dataset enabled the framework's performance to be assessed on a broader range of retinal disease categories. The segmentation Dice coefficient was 78.33±0.15%, while the classification accuracy was 99.10±0.10%. The model's ability to effectively segment retinal fluids and identify retinal lacerations on a different dataset was an excellent demonstration of its generalizability.

An error concerning the transparency of water in J. Opt. Soc. Am. A39, C45 (2022)JOAOD60740-323210.1364/JOSAA.474473 is corrected.

To render realistic material appearances, physically based models often rely on the microfacet theory. These models require several parameters that drive the distribution of microfacet orientations, their reflectance, and a geometric attenuation factor. The latter accounts for self-masking and self-shadowing; it must be managed carefully when physical plausibility is required. The masking term proposed by Smith [IEEE Trans. Antennas Propag.15, 668 (1967)IETPAK0018-926X10.1109/TAP.1967.1138991] is widely used for its accuracy when employed with theoretical distributions. However, it does not ensure exactness when compared with the masking of measured microsurfaces. We have conducted an in-depth study of the error associated with isotropic roughnesses, based on a ray-casting measurement with mesh-based surfaces. This article proposes a correction function that can be added to the theoretical masking term at a very low computation cost while bringing the masking closer to the ground truth. Our correction term is built from a linear combination of two Johnson SB distributions, parameterized according to statistical features of the microsurface. We show that the resulting masking term always reduces the error when compared to the original Smith term alone. This improvement is illustrated in the whole bidirectional reflectance functions with rendered images.

A novel approach is introduced for the design of freeform axisymmetric optical surfaces using an optimization technique based on quadratic Bézier curves. Notably, the continuity (or lack thereof) of the freeform surface produced using the proposed technique is largely unaffected by the source-target mapping function. The validity of the proposed methodology is demonstrated through its application to the design of several laser beam shapers. The results show that the proposed technique requires only a small number of structural points to converge to the optimum design solution. The freeform design method presented herein is mathematically straightforward and can be easily implemented in code. Thus, it offers significant advantages for the design and analysis of a diverse range of optical systems.

The description of light diffraction using catastrophe optics is one of the most intriguing theoretical inventions in the field of classical optics of the last four decades. Its practical implementation has faced some resistance over the years, mainly due to the difficulty of decorating the different (topologically speaking) types of optical singularities (caustics) that concur to build the skeleton on which diffraction patterns stem. Such a fundamental dressing problem has been solved in the past only for the so-called fold, which lies at the bottom of the hierarchy of structurally stable caustics. Climbing this hierarchy implies considerably more challenging mathematical problems to be solved. An ancient mathematical theorem is employed here to find the complete solution of the dressing problem for the cusp, which is placed, in the stable caustic hierarchy, immediately after the fold. The other ingredient used for achieving such an important theoretical result is the paraxial version of the boundary diffraction wave theory, whose tight connection with catastrophe optics has recently been emphasized [Opt. Lett.41, 3114 (2016)OPLEDP0146-959210.1364/OL.41.003114]. A significant example of the developed algorithm, aimed at demonstrating its effectiveness and ease of implementation, is also presented.

Slanted gratings have emerged as a promising area of research due to their distinct properties, such as polarization control, beam steering, and enhanced interactions between light and matter. However, accurately and efficiently modeling these structures, particularly in the case of two-dimensional (2D) slanted gratings, has proven to be challenging. Traditional methods like the Fourier modal method (FMM or RCWA) and finite difference time domain (FDTD) are commonly used but involve approximations of the geometry to accommodate the slant effect. In this study, we address these challenges by employing the polynomial modal method (PMM) for 2D slanted gratings, which, to our knowledge, is a novel approach not previously explored for this type of grating. We introduce a 2D slanted coordinate system to rigorously handle the grating profile. For 2D slanted gratings, the PMM offers several advantages over the FMM, as it overcomes limitations associated with factorization rules and/or staircase approximation of the profile.