Ultra-compact multimode waveguide bend based on a central width controllable dual Bezier structure.

Multimode waveguide bend (MWB) with compactness and high performance is essential for modern mode-division multiplexing (MDM) systems. Here, we present a four-mode MWB optimization method, allowing for direct optimization of the bend's central width, enabling precise control of the MWB's overall curvature variation and preventing excessive narrowing of the waveguide width to suppress loss. The realized bending effective radius is 10 μm, equivalent to the most compact design to date, while being based on a simpler method using dual Bezier mathematical curves.

Programmable Photonic Logic Array Based on Micro-Ring Resonators and All-Optical Modulation.

All-optical computing is an emerging information processing technology. As a cutting-edge technology in the field of photonics, it effectively leverages the unique advantages of photons to achieve rapid computation. However, the lack of a fully functional and programmable design has slowed the progress of this type of optical computing system, especially in optical logic computing.

Cross-domain colorization of unpaired infrared images through contrastive learning guided by color feature selection attention.

In challenging lighting conditions, infrared detectors have become vital tools for enhancing visual perception, overcoming the limitations of visible cameras. However, inherent imaging principles and manufacturing constraints confine infrared imaging systems to grayscale, significantly impacting their utility. In comparison to visible imagery, infrared images lack detailed semantic information, color representation, and suffer from reduced contrast.

Scene-based dual domain non-uniformity correction algorithm for stripe and optics-caused fixed pattern noise removal.

Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN.

Contrast enhancement method in aero thermal radiation images based on cyclic multi-scale illumination self-similarity and gradient perception regularization.

In aerospace, the effects of thermal radiation severely affect the imaging quality of infrared (IR) detectors, which blur the scene information. Existing methods can effectively remove the intensity bias caused by the thermal radiation effect, but they have limitations in the ability of enhancing contrast and correcting local dense intensity or global dense intensity. To address the limitations, we propose a contrast enhancement method based on cyclic multi-scale illumination self-similarity and gradient perception regularization solver (CMIS-GPR).

Ultra-compact optical full-adder based on directed logic and microring resonators.

Photonic integrated circuits with compact design have opened possibilities for the development of optical computing systems; however, the overuse of photonic components in optical designs has slowed the progress of dense integration. In this paper, we propose an ultra-compact optical full-adder based on directed logic and microring resonators. To the best of our knowledge, the proposed structure requires fewer optical components than any other current designs, resulting in a significantly reduced footprint 59.

Real-infraredSR: real-world infrared image super-resolution via thermal imager.

Infrared image super-resolution technology aims to overcome the pixel size limitation of the infrared focal plane array for higher resolution images. Due to the real-world images with different resolutions having more complex degradation processes than mathematical calculation, most existing super-resolution methods using the synthetic data obtained by bicubic interpolation achieve unsatisfactory reconstruction performance in real-world scenes. To solve this, this paper innovatively proposes an infrared real-world dataset with different resolutions based on a refrigerated thermal detector and the infrared zoom lens, enabling the network to acquire more realistic details.

A Universal Digital Lock-in Amplifier Design for Calibrating the Photo-Detector Responses with Standard Black-Bodies.

The lock-in amplifier (LIA) is widely utilized to detect ultra-weak optical periodic signals based on the phase-sensitive and enhanced detecting theory. In this paper, we present an all-digital and universal embedded LIA platform that accurately and conveniently describes the spectrum generated by standard black bodies at various temperatures with different optical detectors. The proposed design significantly reduces the complexity and cost of traditional analog LIAs while maintaining accuracy.

Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS.

The existence of non-uniformity in infrared detector output images is a widespread problem that significantly degrades image quality. Existing scene-based non-uniformity correction algorithms typically struggle to balance strong non-uniformity correction with scene adaptability. To address this issue, we propose a novel scene-based algorithm that leverages the frequency characteristics of the non-uniformity, combine and improve single-frame stripe removal, multi-scale statistics, and least mean square (LMS) methods.

Enhancing infrared imaging systems with temperature-dependent nonuniformity correction via single-frame and inter-frame structural similarity.

Temperature-dependent nonuniformity in infrared images significantly impacts image quality, necessitating effective solutions for intensity nonuniformity. Existing variational models primarily rely on gradient prior constraints from single-frame images, resulting in limitations due to insufficient exploitation of intensity characteristics in both single-frame and inter-frame images. This paper introduces what we believe to be a novel variational model for nonuniformity correction (NUC) that leverages single-frame and inter-frame structural similarity (SISB).

Polarized skylight compass based on a soft-margin support vector machine working in cloudy conditions.

The skylight polarization pattern, which is a result of the scattering of unpolarized sunlight by particles in the atmosphere, can be used by many insects for navigation. Inspired by insects, several polarization navigation sensors have been designed and combined with various heading determination methods in recent years. However, up until now, few of these studies have fully considered the influences of different meteorological conditions, which play key roles in navigation accuracy, especially in cloudy weather.

Automatic Lumbar Spine Tracking Based on Siamese Convolutional Network.

Deep learning has demonstrated great success in various computer vision tasks. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of the spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. The aim of this work is to automatically track lumbar vertebras with rotated bounding boxes in DVFI sequences.

Object Tracking Based on Vector Convolutional Network and Discriminant Correlation Filters.

Due to the fast speed and high efficiency, discriminant correlation filter (DCF) has drawn great attention in online object tracking recently. However, with the improvement of performance, the costs are the increase in parameters and the decline of speed. In this paper, we propose a novel visual tracking algorithm, namely VDCFNet, and combine DCF with a vector convolutional network (VCNN).

Automatic Lumbar MRI Detection and Identification Based on Deep Learning.

The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning.

High spatial resolution recording of near-infrared hologram based on photo-induced phase transition of vanadium dioxide film.

We present a method to record near-infrared (NIR) hologram at high spatial resolution. This method up-converts the NIR holograms to visible holograms taking advantage of the photo-induced phase transition characteristic of vanadium dioxide (VO) material, and subsequently, the visible holograms are recorded by a high-resolution visible CMOS sensor. Obviously the pitch of visible sensor is much smaller than NIR sensors, so our method can extremely increase the recording resolution of NIR holograms.

Optimized pulse width modulation pattern strategy for three-dimensional profilometry with projector defocusing.

Three-dimensional profilometry by sinusoidal fringe projection using phase-shifting algorithms is usually distorted by the nonlinear intensity response of commercial video projectors. To overcome this problem, several methods including sinusoidal pulse width modulation (SPWM) were proposed to generate sinusoidal fringe patterns with binary ones by defocusing the project to some certain extent. However, the residual errors are usually nonnegligible for highly accurate measurement fields, especially when the defocusing level is insufficient.

Scene-based nonuniformity correction algorithm based on interframe registration.

In this paper, we present a simple and effective scene-based nonuniformity correction (NUC) method for infrared focal plane arrays based on interframe registration. This method estimates the global translation between two adjacent frames and minimizes the mean square error between the two properly registered images to make any two detectors with the same scene produce the same output value. In this way, the accumulation of the registration error can be avoided and the NUC can be achieved.