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). First, we conceive to correct for intensity bias by amplifying gradient. Specifically, we propose a gradient perception regularization (GPR) solver to correct intensity bias by directly decomposing degraded image into a pair of high contrast images, which do not contain intensity bias and exhibit inverted intensity directions. However, we find that the GPR fails for dense intensity area due to small gradient of the scene. Second, to cope with the cases of dense intensity, we regard the dense intensity bias as the sum of multiple slight intensity bias. Then, we construct a cyclic multi-scale illumination self-similarity (CMIS) model by using multi-scale Gaussian filters and structural similarity prior to removing the dense intensity layer by layer. The result acts as coarse correction for GPR, which does not need to be overly concerned with whether the result has intensity residuals or not. Finally, the coarse corrected result is input to the GPR module to further correct residual intensity bias by enhancing contrast. Extensive experiments in real and simulated data have demonstrated the superiority of the proposed method.

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 59.2µm×29.2µm. Also, the proposed structure exhibits a maximum delay time of approximately 10 ps, implying a minimum date rate of 100 GHz. Simulation results by finite-difference time-domain (FDTD) demonstrate the effectiveness and feasibility of the proposed optical full-adder.

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. We obtain images under different fields of view by adjusting the infrared zoom lens and then achieve the scale and luminance alignment of high and low-resolution (HR-LR) images. This dataset can be used for infrared image super-resolution, with an up-sampling scale of two. In order to learn complex features of infrared images efficiently, an asymmetric residual block structure is proposed to effectively reduce the number of parameters and improve the performance of the network. Finally, to solve the slight misalignment problem in the pre-processing stage, contextual loss and perceptual loss are introduced to improve the visual performance. Experiments show that our method achieves superior results both in reconstruction effect and practical value for single infrared image super-resolution in real scenarios.

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. The LIA components are implemented using a single field programmable gate array (FPGA), offering flexibility to modify parameters for different situations. The normalized mean-square error (NMSE) of the captured spectra in the experiments is within 0.9% compared the theoretical values.

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). This approach capitalizes on the structural similarities between the corrected image, intensity bias map, and degraded image, facilitating efficient suppression of intensity nonuniformity in real-world scenarios. The proposed method diverges fundamentally from existing strategies and demonstrates superior performance in comparison with state-of-the-art correction models.

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. Following the "coarse-to-fine" correction process, the coarse correction stage introduces an adaptive progressive correction strategy based on Laplacian pyramids. By improving 1-D guided filtering and high-pass filtering to shape high-frequency sub-bands, non-uniformity can be well separated from the scene, effectively suppressing ghosting. In the fine correction stage, we optimize the expected image estimation and spatio-temporal adaptive learning rates based on guided filtering LMS method. To validate the efficacy of our algorithm, we conduct extensive simulation and real experiments, demonstrating its adaptability to various scene conditions and its effectiveness in correcting strong non-uniformity.

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. Therefore, this study makes a major contribution to the study on bio-inspired heading determination by designing a skylight compass method to suppress cloud disturbances. The proposed method transforms the heading determination problem into a binary classification problem by segmentation, connected component detection, and inversion. Considering the influences of noise and meteorological conditions, the binary classification problem is solved by the soft-margin support vector machine. In addition, to verify this method, a pixelated polarization compass platform is constructed that can take polarization images at four different orientations simultaneously in real time. Finally, field experimental results show that the designed method can more effectively suppress the interference of clouds compared with other methods.

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. Instead of distinguishing vertebras using annotated lumbar images or sequences, we train a full-convolutional siamese neural network offline to learn generic image features with transfer learning. The siamese network is trained to learn a similarity function that compares the labeled target from the initial frame with the candidate patches from the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. Our tracker is performed by evaluating the candidate rotated patches sampled around the previous target's position and presents rotated bounding boxes to locate the lumbar spine from L1 to L4. Results indicate that the proposed tracking method can track the lumbar vertebra steadily and robustly. The study demonstrates that the lumbar tracker based on siamese convolutional network can be trained successfully without annotated lumbar 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). We replace one traditional convolutional filter with two novel vector convolutional filters in the convolutional stage of our network. This enables our model with few memories (only 59 KB) trained offline to learn the generic image features. In the online tracking stage, we propose a coarse-to-fine search strategy to solve drift problems under fast motion. Besides, we update model selectively to speed up and increase robustness. The experiments on OTB benchmarks demonstrate that our proposed VDCFNet can achieve a competitive performance while running over real-time speed.

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. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image. In the convolutional neural network, a contrast object will not update during frames, which allows a fast speed and saves memory. Due to its distinctive shape, S1 is firstly detected and a rough region around it is extracted for searching for L1-L5. The results are evaluated with accuracy, precision, mean, and standard deviation (STD). Finally, our detection algorithm achieves the accuracy of 98.6% and the precision of 98.9%. Most failed results are involved with wrong S1 locations or missed L5. The study demonstrates that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images. It can be believed that our detection method will assist clinicians to raise working efficiency.

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 (VO2) 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. The experiments demonstrate the effectiveness of our method. Our method can improve the viewing angle of NIR holography to observe large-scale objects and shorten the observation distance so that the application area of NIR holography is expanded. It has the potential to become a more effective NIR hologram recording method.

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. In this work, we propose two novel methods to further improve the defocusing technique. We find that by properly optimizing SPWM patterns according to some criteria, and combining SPWM technique with four-step phase-shifting algorithm, the dominant undesired harmonics will have no impact on the phase obtained. We also propose a new sinusoidal fringe generation technique called tripolar SPWM, which can generate ideal sinusoidal fringe patterns with a very small degree of defocusing. Simulations and experiments are presented to verify the performance of these two proposed techniques.

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. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of the proposed technique is thoroughly studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows a significantly fast and reliable fixed-pattern noise reduction and obtains an effective frame-by-frame adaptive estimation of each detector's gain and offset.