This paper firstly analyzes the basic principle of generating fractal art graphics and the artistic features of graphics and then uses scientific visualization method to innovate and improve the theoretical model used in this paper. The generation principle and graphic characteristics of fractal graphics of complex dynamic system and L-system are mainly analyzed, and two kinds of art graphics-flower art graphics and geometric art graphics-have been developed. On this basis, the generated artistic figures are designed for the second time and then applied to the design of clothing patterns. By using MATLAB programming software to generate art graphics conforming to a specific style, combined with image processing software Photoshop to process and redesign the generated graphics, these art graphics can assist the design of clothing printing patterns and make patterns applicable for clothing fabrics. Finally, the fractal pattern theory is applied to silk scarves design and clothing fabric design through digital printing technology, which can fully reflect the practicability and superiority of clothing pattern design based on the fractal theory. Based on the experimental result, it shows that it is completely feasible to design clothing fabric printing patterns based on fractal theory, and the unusual artistic patterns designed have very considerable practical value. In addition, this method encourages creativity in the garment pattern design process and accelerates new design generation.

One-shot image classification aims to train image classifiers over the dataset with only one image per category. It is challenging for modern deep neural networks that typically require hundreds or thousands of images per class. In this paper, we adopt metric learning for this problem, which has been applied for few- and many-shot image classification by comparing the distance between the test image and the center of each class in the feature space. However, for one-shot learning, the existing metric learning approaches would suffer poor performance because the single training image may not be representative of the class. For example, if the image is far away from the class center in the feature space, the metric-learning based algorithms are unlikely to make correct predictions for the test images because the decision boundary is shifted by this noisy image. To address this issue, we propose a simple yet effective regression model, denoted by RestoreNet, which learns a class agnostic transformation on the image feature to move the image closer to the class center in the feature space. Experiments demonstrate that RestoreNet obtains superior performance over the state-of-the-art methods on a broad range of datasets. Moreover, RestoreNet can be easily combined with other methods to achieve further improvement.

The accumulated errors in multi-station measurements make it challenging to meet higher precision requirements for annular or straight tunnel measurements in the accelerator alignment field. In this paper, a study on fixing positions and orientations of laser trackers during the bundle adjustment is presented. First, we adopt the mixed least squares-total least squares algorithm to calculate the spatial-transformation parameters. Then, according to the principle of bundle adjustment based on laser trackers, we design four schemes and compare them to determine which scheme results in the highest precision. Experimental results show that fixing each station’s position and orientation can considerably decrease the absolute point errors from 0.130 to 0.069 mm within 15 m × 10 m × 3 m, and fixing stations’ orientations is better than fixing their positions. This research can derive high-precision schemes that effectively reduce accumulated errors in annular or straight tunnel measurements, and it can apply to other robotic sensors as well.

To accelerate the scanning speed of magnetic resonance imaging (MRI) and improve the quality of magnetic resonance (MR) image reconstruction, a fast MRI technology based on compressed sensing is proposed. Nesterov's accelerated gradient descent (NAG) algorithm uses Nesterov acceleration to optimize the gradient descent (GD) method. However, this form of acceleration factor uses a fixed iteration curve update and can not adapt to different iteration processes. A generalized Nesterov acceleration concept is proposed. Combining the total variation model, a generalized Nesterov accelerated conjugate gradient based on total variation (GNACG_TV) algorithm is proposed. It extends the acceleration factor in a generalized manner, introducing the Frobenius norm of the objective function as a parameter, so that the acceleration factor is related not only to the number of iterations but also to the iteration process and guarantees the convergence of the iterative process. Experiments on three MR images (abdomen, head, and ankles) at different sampling ratios show that the proposed GNACG_TV algorithm compares favorably with conjugate gradient (CG), conjugate gradient based on total variation (CG_TV), Nesterov accelerated conjugate gradient based on total variation (NACG_TV), and conjugate gradient based on adaptive moment estimation (ADAMCG) algorithms in the MSE, PSNR and SSIM exhibit better performance and robustness in denoising performance for the proposed algorithm. Comparing with the result of qualitative and quantitative analysis, it was concluded that the proposed method can better reconstruct under-sampled MR images than other 4 methods. GNACG_TV can further improve the convergence speed based on Nesterov acceleration and get better reconstruction performance. INDEX TERMS Compressed sensing, conjugate gradient, generalized Nesterov acceleration, MR image reconstruction.

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