(Retracted) Image restoration and quantitative metallographic tissue based on machine vision

Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.

As a detection tool to identify metal or alloy, metallographic quantitative analysis has received increasing attention for its ability to evaluate quality control and reveal mechanical properties. The detection procedure is mainly operated manually to locate and characterize the constitution in metallographic images. The automatic detection is still a challenge even with the emergence of several excellent models. Benefiting from the development of deep learning, with regard to two different metallurgical structural steel image datasets, we propose two attention-aware deep neural networks, Modified Attention U-Net (MAUNet) and Self-adaptive Attention-aware Soft Anchor-Point Detector (SASAPD), to identify structures and evaluate their performance. Specifically, in the case of analyzing single-phase metallographic image, MAUNet investigates the difference between low-frequency and high-frequency and prevents duplication of low-resolution information in skip connection used in an U-Net like structure, and incorporates spatial-channel attention module with the decoder to enhance interpretability of features. In the case of analyzing multi-phase metallographic image, SASAPD explores and ranks the importance of anchor points, forming soft-weighted samples in subsequent loss design, and self-adaptively evaluates the contributions of attention-aware pyramid features to assist in detecting elements in different sizes. Extensive experiments on the above two datasets demonstrate the superiority and effectiveness of our two deep neural networks compared to state-of-the-art models on different metrics.

An investigation has been made on the infiltration behaviour in rapid prototyping of Al alloy parts under different infiltration atmospheres. An Al 6061 alloy preform was first prepared by selective laser sintering. Then the aluminium precursor powders in the preform were converted into AlN to form a skeletal AlN structure, which was subsequently pressureless infiltrated with a molten Al 6061 alloy under nitrogen, argon or vacuum. The pathway of the infiltrant was determined by density measurements in conjunction with metallographic examination and quantitative image analysis. Detailed comparison of the microstructures at the surface and the centre of the as-infiltrated samples indicates that the pathway into the porous preform is dependent on the infiltration atmosphere. Under vacuum, the infiltrant fills the interior of the preform first and then propagates to the surface. In contrast, under nitrogen or argon, the infiltrant penetrates along the surface prior to filling the interior.

To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.

Underwater laser imaging is of great significance in underwater search and marine science, <i>etc</i>. However, traditional underwater laser imaging is often of poor quality with noises and blurs, moreover, the resolution of the image is also low. In order to obtain clear underwater images with high resolution and quality, here, we have designed a range gated imaging underwater imaging system and realized an image restoration approach. In this paper, based on the introduction to the imaging system and image restoration algorithm, the experiment is established by setting the imaging system under water in the lake to capture the underwater targets. With the proposed underwater image restoration approach, images of high quality could be retrieved which proves that the method is able to identify the target ~10 meters away underwater.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery is guaranteed to converge, but it converges slowly. Its ordered-subset version (OS-EM) is used widely in tomographic image reconstruction because of its order-of-magnitude acceleration compared with the EM algorithm, but it does not guarantee convergence. Recently the ordered-subset, separable-paraboloidal-surrogate (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. We adapt the relaxed OS-SPS algorithm to the problem of image restoration. Because data acquisition in image restoration is different from that in tomography, we employ a different strategy for choosing subsets, using pixel locations rather than projection angles. Simulation results show that the relaxed OS-SPS algorithm can provide an order-of-magnitude acceleration over the EM algorithm for image restoration. This new algorithm now provides the speed and guaranteed convergence necessary for efficient image restoration.

Alpha-methylacyl-CoA racemase (AMACR) is an enzyme involved in the metabolism of fatty acids and is an important tissue biomarker in the prostate to distinguish normal glands from prostate cancer. Here, for the first time, we evaluated the expression of AMACR protein in normal breast, ductal carcinoma in situ, and invasive carcinomas. By immunofluorescence and immunohistochemistry, AMACR was seen in cytoplasmic granules consistent with a mitochondrial and peroxisomal localization. AMACR expression was determined by immunohistochemistry on 160 invasive carcinomas with long follow-up, using a high-density tissue microarray, and evaluated by two methods: standard pathology review and quantitative image analysis. AMACR was overexpressed in 42 of 160 (26%) invasive carcinomas, and it was associated with a decrease in tumor differentiation, a feature of aggressive breast cancer. Quantitative analysis allowed for better discrimination and more accurate evaluation of low-intensity staining. In conclusion, AMACR protein is expressed in normal breast and its expression seems to increase in invasive carcinomas. We observed stronger AMACR protein expression in high-grade carcinomas when compared with low-grade ones. Quantitative image analysis is a novel way to accurately and reproducibly evaluate immunohistochemistry in breast tissue samples using high-density tissue microarrays.

Aiming at correcting the severe chromatic aberration of the long focal lens, the traditional optical design often requires employing complex structure, introducing special dispersive glasses or even utilizing the hybrid refractive-diffractive imaging method. However, complex structure will bring several drawbacks such as large volume and heavy weight. Special glasses and refractive-diffractive hybrid imaging will greatly increase the cost, which hardly meets the needs of miniaturization and low cost of optical imaging module. Although image restoration algorithm is commonly used to optimize the image quality to a certain extent, the optical design and image restoration process are independent of each other. Therefore, it is difficult to ensure the high resolution of the image while realizing the light weight, small volume and low cost of the optical system simultaneously. Utilizing the computational imaging theory, a simple long-focus optical system design method based on the optical/image co-design is proposed and deomonstrated in this paper. On the basis of the idea of global optimization, the reported approach considers the two independent links of optical design and image restoration as a combination. The imaging quality requirements in the optical system design are relaxed at the front end, and the image restoration algorithm is used to remove the residual aberrations in the back end. This method can not only obtain the same or even higher imaging performance, but also reduce the complexity of the optical system.

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Region extraction and labeling is a fundamental process in building computer systems that automatically analyze and interpret images and image sequences of a scene. It involves extracting meaningful regions and their attributes from an image that are of interest in a given domain, e.g., a human face in face recognition and surveillance applications, a vehicle in intelligent highway monitoring, or an industrial tool in automated manufacturing and inspection applications. In this paper, we present a generalized and fast region labeling algorithm that can be applied to numerous image processing tasks: region extraction in binary images, region segmentation from gray scale or color images, region boundary extraction, chain code extraction, run-length coding of images, and image restoration from chain codes. The general applicability of our algorithm stems from its strengths in identifying the fundamental and common computational steps and providing robust and efficient procedures to accomplish these steps without requiring multiple scans of images. Our algorithm is shown to be more effective in terms of space and computational efficiency, in comparison with many existing approaches to region labeling and extraction. Results from our experiments with over 5,000 frames obtained from MPEG-1 video bitstreams demonstrate the good performance of our algorithm in terms of video text segmentation accuracy and computational efficiency.

End‐to‐end optimization of metalens and artificial intelligence‐driven image restoration algorithms has recently emerged as a powerful tool for realizing ultra‐compact imaging systems. However, the limited imaging quality of existing approaches remains challenging in meeting the demand for commercial devices due to the severe aberrations exhibited by metalens. These results in highly blurred sensor images, creating substantial challenges for accurate image restoration. In this work, a novel meta‐imager is introduced that overcomes this challenge by employing an aperture‐stop‐integrated metalens and co‐designing it with a computational image restoration network using a fully differentiable optimization framework. The proposed imager physically consists of a single metalens and an aperture stop located on the opposite side of the 1 mm‐thick glass substrate. This configuration effectively alleviates off‐axis aberrations such as coma and astigmatism, facilitating the image restoration process of the deep neural networks. The experimental results present that this scheme features 70° field‐of‐view, for full‐color imaging across the entire visible spectrum. It is believed that this work represents a significant advancement in creating ultra‐compact cameras using nanophotonics.

The expectation-maximization (EM) algorithm for maximum-likelihood image recovery converges very slowly. Thus, the ordered subsets EM (OS-EM) algorithm has been widely used in image reconstruction for tomography due to an order-of-magnitude acceleration over the EM algorithm. However, OS-EM is not guaranteed to converge. The recently proposed ordered subsets, separable paraboloidal surrogates (OS-SPS) algorithm with relaxation has been shown to converge to the optimal point while providing fast convergence. In this paper, we develop a relaxed OS-SPS algorithm for image restoration. Because data acquisition is different in image restoration than in tomography, we adapt a different strategy for choosing subsets in image restoration which uses pixel location rather than projection angles. Simulation results show that the order-of-magnitude acceleration of the relaxed OS-SPS algorithm can be achieved in restoration. Thus the speed and the guarantee of the convergence of the OS algorithm is advantageous for image restoration as well.

There is distortion phenomenon in image emerge, transmit and record. Image restoration is a process which recover bad image into original image. When we use genetic algorithm for image restoration, there will be premature problem. The paper discusses a new algorithm for image restoration based on combination of parallel genetic algorithm with Hopfield neural network, take the advantage of parallel GA parameter selection and then use Hopfield NN to train sample efficiently. Experiments demonstrate that this optimization method in this paper will overcome premature problem and run more rapidly, as a result obtain a better recovery image.

A rapid hybrid algorithm for image restoration combining parametric Wiener filtering and wave atom transform