For a single optical sparse aperture imaging system, because of the sub-mirrors’ dispersion and sparsity, the modulation transfer function (MTF) is unlikely to satisfy all frequencies due to inadequate sampling of the discrete sub-apertures. Thus, a baseline adjustable optical sparse aperture imaging system is proposed by expanding the sub-aperture array. Sufficient information from separate frequency regions can be maintained and fused. An improved Wiener filter algorithm based on the system's transfer function and noise features is designed for image restoration. Both the simulation and experiment prove that the proposed method can achieve a satisfactory MTF level and a clear reconstruction effect. The average peak signal to noise ratio is raised from 22.53 dB of degraded images to 29.17 dB of the restored images. The structural similarity index of the results is increased from 0.65 to 0.92. And compared with several conventional algorithms, scores of multiple evaluation indexes of the proposed method is the highest.
KEYWORDS: Pose estimation, Cameras, Feature extraction, Calibration, Education and training, Data modeling, Bone, Imaging systems, 3D metrology, Information fusion
Traditional 3D human pose estimation algorithms are often influenced by the accuracy of 2D keypoint detection and camera calibration, and they struggle to handle low-resolution and occluded scenes. To address these challenges, we propose a multi-view 3D human pose estimation method that incorporates prior information. At the 2D pose extraction stage, we design a bottom-up detection network called HRPifPaf to achieve accurate human pose detection in low-resolution scenarios. It first constructs a high-resolution feature extraction module that combines features from different scales. Then, a joint prediction and association module combines confidence scores and scale factors with vector directions pointing to the main body parts of the joints. We also utilize a Kalman filter to optimize the final detection results. At the 3D pose synthesis stage, we propose a multi-camera parameter joint optimization calibration method that leverages prior information of the human skeleton to address challenges such as body occlusion and inaccurate camera intrinsic and extrinsic parameters, designing a comprehensive cost function based on reprojection error, and human body geometry constraints.
The existence of noise will seriously affect quality of image. Image denoising method is important for obtaining high-quality image. Most of traditional denoising methods need to estimate the noise level, which are unstable in the actual denoising scene. As a data-driven method, the development of deep learning shows great potential in the field of image denoising. In this paper, a method combining image denoising model and deep learning framework is proposed. Welldesigned multi-scale restoration network Noise-Net embedded this method optimizing neural network training to obtain ideal image recovery results. By down-sampling the original noisy image input at different scales, the noisy image features are extracted. These multi-scale features are summed and combined. The addition of the residual module improves the network training ability and effectively prevents the network from overfitting. The network is optimized by Convolutional Block Attention Module (CBAM). It can enable effective extraction of image features in the spatial and frequency domains. Network input is noisy image, clear image as label. The training phase is divided into two stages: noisy data generation and simulated images for pre-training. 2000 images of DOTA 1.0 dataset constitute as training set and 1000 images as test set. By adding different noises such as Gaussian noise and Poisson noise to the image, the data set is constructed with the label image. The loss function of the absolute minimum error is calculated and sent to the Adam optimizer for parameter optimization. Numerical simulation and experimental results show that Noise-Net has an effect on image denoising ability.
Image stitching is a technology that combines multiple images taken by different cameras to create a larger field of view. It has wide applications in scenarios such as surveillance and virtual reality, making it an important topic in computer vision. This paper addresses the challenges associated with stitching ground images with inconspicuous features using traditional methods. These challenges include poor feature extraction capabilities and issues like misalignment, artifacts, and structural deformations introduced during stitching. The paper proposes the utilization of unsupervised learning techniques to enhance the quality of image stitching. The network primarily consists of two parts: image alignment and image reconstruction. In the image reconstruction part, deformation rules for image stitching are learned through both a low-resolution branch and a high-resolution branch. Finally, it evaluates the stitched images before and after improvement using image stitching evaluation metrics. Experimental results demonstrate that this approach successfully mitigates artifacts and distortions introduced during stitching, resulting in an improved image stitching quality.
Camera calibration is one of the key tasks in the field of computer vision, finding extensive applications in various domains, including photogrammetry, 3D reconstruction, augmented reality, and autonomous driving. The Direct Linear Transform (DLT) algorithm, a classical approach for camera calibration, estimates camera parameters by solving a system of linear equations. However, traditional DLT methods may face accuracy and stability issues when dealing with noise, distortion, and nonlinear effects. To address these limitations, this paper introduces a camera calibration method based on the Improved DLT algorithm. This method incorporates distortion models into the traditional DLT algorithm and utilizes Levenberg-Marquardt (LM) optimization techniques to enhance calibration accuracy and stability. The key steps involve data preparation, DLT estimation, and nonlinear optimization. Experimental results demonstrate that the Improved DLT algorithm outperforms traditional DLT methods, particularly in cameras with significant distortion and wide field of view. It exhibits smaller reprojection errors and achieves a more uniform error distribution, especially at image edges. This research contributes by providing a more accurate and robust camera calibration method, offering valuable tools for computer vision applications, and advancing the development and application of computer vision technology.
While mobile phones offer convenience in our daily lives, they also introduce associated security risks. For instance, in high-security settings like confidential facilities, casual mobile phone usage and calls can inadvertently lead to the leakage of sensitive information. In response to such security concerns, this paper proposes an algorithm for recognizing mobile phone behaviors in high-resolution images with a wide field of view.To improve inference speed, we introduce the C3_Faster module. To address the challenge of detecting small-sized targets in images, we propose a boundary loss function. This reduces the scale sensitivity of IoU loss and mitigates model underperformance in detecting small objects. Experimental results demonstrate that, our improved algorithm achieved a 7.6% increase in mAP and a 38% improvement in inference speed. These findings highlight the effectiveness of our enhanced algorithm, making it well-suited for the task of mobile behavior recognition in secure environments.
Diabetic foot ulcers are a significant complication that afflicts diabetes patients, posing a serious threat to their quality of life and health. However, challenges persist in the current landscape of risk assessment and treatment prognosis regarding diabetic foot ulcers. Hyperspectral imaging, an advanced non-invasive detection technology, has garnered considerable attention for its potential in assessing and predicting the development risk and non-invasive healing possibilities of diabetic foot ulcers. Given the challenges in collecting hyperspectral datasets, this paper visually reconstructs hyperspectral images from the available optical data for diabetic foot ulcers. It visualizes the oxygen saturation in the ulcer region and concludes that the average oxygen saturation for ulcers that eventually healed falls within the range of 54% to 64%, while for ulcers that did not heal, the average oxygen saturation ranges from 32% to 48%.
Hollow-Core Fiber (HCF) has attracted great interest from researchers because of its high damage threshold and small nonlinearity compared with solid-core fiber. However, how to reduce the loss of HCF has always been an urgent problem to be solved. Aiming to solve the problem, we propose a novel Hollow-Core Negative Curvature Fiber (HC-NCF) with an elliptical nested tube and a circular nested tube. The structure of this HC-NCF is relatively simple, which greatly reduces the difficulty of fabrication. Finite element modeling has been used to simulate and calculate the Confinement Loss (CL) and Bending Loss (BL) of the fiber with different nested tube structures. Results show that the CL of the LP01 mode is as low as 6.48×10-6 dB/km at the interesting wavelength of 1.06 μm. It exhibits a minimum CL of 5.28×10-6 dB/km at 1.01 μm with maintaining a loss of less than 0.003 dB/km over 1020 nm (0.77 μm to 1.79 μm) bandwidth. In addition, we proposed the HC-NCF has been confirmed to have better-bending resistance. Within a bending radius of 5–40 cm, the HC-NCF has a BL below 3.75×10-4 dB/km at a 10 cm bending radius; the BL is below 1.03×10-5 dB/km at a 40 cm bending radius.
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