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Fractal dimension of any defect in microstructure is a critical parameter for their geometric irregularities, pattern of arrangements and morphological attributes. The fractal dimensions of δ -hydride network (circumferential and radial) at different hydrogen concentrations of a Zr–2.5Nb pressure tube have been determined and correlated with its respective microstructural and textural variables as a function of stress-state. Image texture analysis has also been carried out to realize the stored energy and configurational stability of the δ -hydride pattern under α -Zr matrix. The invasive fractal nature, chaos and reorientation of δ -hydride as a function of processing schedule, stress-state and hydrogen content has been convincingly revealed. This research will be extremely useful in estimating primarily the severity of hydrogen embrittlement of a nuclear component by simply analyzing the fractal pattern and image texture of such hydride networks. [Display omitted] • Fractal dimensions of complex δ -hydride network measured. • Zr–2.5Nb alloy at different processing schedules, hydrogen concentrations chosen. • Fractal dimension of δ -hydride, crystallographic texture and stress-state correlated. • Image texture analysis performed to understand the stored-energy, configurational stability and reorientation of δ -hydride. • The invasive fractal nature, chaos and reorientation of δ -hydride convincingly revealed. [ABSTRACT FROM AUTHOR]

This work focuses on image anomaly detection by leveraging only normal images in the training phase. Most previous methods tackle anomaly detection by reconstructing the input images with an autoencoder (AE)-based model, and an underlying assumption is that the reconstruction errors for the normal images are small, and those for the abnormal images are large. However, these AE-based methods, sometimes, even reconstruct the anomalies well; consequently, they are less sensitive to anomalies. To conquer this issue, we propose to reconstruct the image by leveraging the structure-texture correspondence. Specifically, we observe that, usually, for normal images, the texture can be inferred from its corresponding structure (e.g., the blood vessels in the fundus image and the structured anatomy in optical coherence tomography image), while it is hard to infer the texture from a destroyed structure for the abnormal images. Therefore, a structure-texture correspondence memory (STCM) module is proposed to reconstruct image texture from its structure, where a memory mechanism is used to characterize the mapping from the normal structure to its corresponding normal texture. As the correspondence between destroyed structure and texture cannot be characterized by the memory, the abnormal images would have a larger reconstruction error, facilitating anomaly detection. In this work, we utilize two kinds of complementary structures (i.e., the semantic structure with human-labeled category information and the low-level structure with abundant details), which are extracted by two structure extractors. The reconstructions from the two kinds of structures are fused together by a learned attention weight to get the final reconstructed image. We further feed the reconstructed image into the two aforementioned structure extractors to extract structures. On the one hand, constraining the consistency between the structures extracted from the original input and that from the reconstructed image would regularize the network training; on the other hand, the error between the structures extracted from the original input and that from the reconstructed image can also be used as a supplement measurement to identify the anomaly. Extensive experiments validate the effectiveness of our method for image anomaly detection on both industrial inspection images and medical images. [ABSTRACT FROM AUTHOR]

Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images of several other related diseases affecting the lungs. This work represents an initial experimentation using image texture feature descriptors, feed-forward and convolutional neural networks on newly created databases with COVID-19 images. The goal was setting a baseline for the future development of a system capable of automatically detecting the COVID-19 disease based on its manifestation on chest X-rays and computerized tomography images of the lungs. [ABSTRACT FROM AUTHOR]

In order to show high dynamic range (HDR) images by traditional displays, various tone-mapping operators have been designed to convert HDR images into low dynamic range (LDR) images recently. However, how to estimate the visual quality of LDR images effectively is still challenging. In this paper, we propose a novel blind quality assessment method for tone-mapped images with the consideration of naturalness and the perceptual characteristics of human visual system (HVS). First, we design parametric models that describe characteristics of chromatic information in tone-mapped images and extract quality-aware features based on global statistics model to characterize the naturalness of tone-mapped images. Second, motivated by perceptual characteristics that the HVS is highly adaptive to the image texture, we employ local texture features to capture the quality degradation of tone-mapped images. Support vector regression (SVR) is used to train the quality prediction model from features to human ratings. Experimental results indicate that the proposed metric can get better performance in predicting the visual quality of tone-mapped images than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Color image super-resolution reconstruction based on the sparse representation model usually adopts the regularization norm (e.g., L1 or L2). These methods have limited ability to keep image texture detail to some extent and are easy to cause the problem of blurring details and color artifacts in color reconstructed images. This paper presents a color super-resolution reconstruction method combining the L2/3 sparse regularization model with color channel constraints. The method converts the low-resolution color image from RGB to YCbCr. The L2/3 sparse regularization model is designed to reconstruct the brightness channel of the input low-resolution color image. Then the color channel-constraint method is adopted to remove artifacts of the reconstructed highresolution image. The method not only ensures the reconstruction quality of the color image details, but also improves the removal ability of color artifacts. The experimental results on natural images validate that our method has improved both subjective and objective evaluation. [ABSTRACT FROM AUTHOR]

The study of deep learning-based fast magnetic resonance imaging (MRI) reconstruction methods has become popular in recent years. However, it is still challenging to obtain high-fidelity MRI images from undersampled data with large acceleration factors. The objective of this study was to improve the reconstruction quality of under-sampled MR images by exploring the correlation between pixels in the image and the perceptual similarities between the initial reconstructed images and the fully sampled ones. Existing reconstruction methods often suffer from aliasing artefacts and blurry texture information. We propose a novel perceptual residual self-attention generative adversarial model, SRSAGAN, to tackle this issue for fast and accurate MRI reconstruction. The proposed method involves two operation steps. In step 1, leveraging the average information and similarity weights of high-level features, we designed a novel feature enhancement module based on residual self-attention to restore fine texture details in the reconstructed image. In step 2, to improve the accuracy of network inference, we compare the perceptual similarities between the initial reconstructed images and the fully sampled ones. The proposed algorithm was evaluated using the fastMRI dataset. It demonstrates that SRSAGAN outperforms state-of-art methods and reconstructs complex image texture with only 10 % retained the k-space raw data (10-fold acceleration). The reconstruction time is only about 7.78 ms. [ABSTRACT FROM AUTHOR]

Aiming at the defect of local binary pattern (LBP), this paper presents a novel and efficient local second-order derivative descriptor for image texture feature extraction based on local convex-and-concave pattern (LCCP). LCCP encodes local directional second-order derivative feature of texture based on local convex-and-concave characteristic, which can really capture the derivative information of texture than the center symmetric local derivative pattern (CS-LDP) does. The proposed multi-resolution LCCP not only encodes directional derivative information but also constructs spatial relationships in a given local region by bilinear interpolation. Extensions of multi-resolution LCCP based on uniform pattern and weighted pattern have also been discussed. The performance of the proposed LCCP and its extensions are assessed on the biometric recognition and texture classification under different challenges. Each image is divided into several regions from which histogram features are extracted and concatenate into an enhanced feature vector for match. Extensive experiments have validated the effectiveness of LCCP and its extensions. [ABSTRACT FROM AUTHOR]

As synthetic aperture radar (SAR) has been widely used nearly in every field, SAR image de-noising became a very important research field. A new SAR image de-noising method based on texture strength and weighted nuclear norm minimization (WNNM) is proposed. To implement blind de-noising, the accurate estimation of noise variance is very important. So far, it is still a challenge to estimate SAR image noise level accurately because of the rich texture. Principal component analysis (PCA) and the low rank patches selected by image texture strength are used to estimate the noise level. With the help of noise level, WNNM can be expected to SAR image de-noising. Experimental results show that the proposed method outperforms many excellent de-noising algorithms such as Bayes least squares-Gaussian scale mixtures (BLS-GSM) method, non-local means (NLM) filtering in terms of both quantitative measure and visual perception quality. [ABSTRACT FROM AUTHOR]

Classification is a critical step to make full use of the hyperspectral data. The most current approaches perform well for analyzing the macro texture, but they often fail to deal with the micro texture. Thus, this study proposes a general framework for the material with micro texture based on Hyperspectral Image (HSI) technique. In this framework, Local Response Pattern (LRP) is firstly proposed to describe 2D image texture to preserve more structural information and keep less sensitive to image conditions. Then, LRP is extended to represent HSI with Texture Enhancement (TE) by considering opponent relationships between pairs of bands. After that, Discriminated Locality Preserving Projection (DLPP) is proposed to reduce data dimension in a linearizing nonlinear manifold way. Finally, experiments on the hyperspectral images of fresh and frozen-thawed fish fillets are conducted. The results demonstrate that the proposed framework is efficient in terms of both recognition rates and robustness. [ABSTRACT FROM AUTHOR]

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern encoding descriptor for visual texture classification. First, we develop local extremum patterns and local center pattern to represent the neighborhood intensity changes. Then, we design a compact encoding scheme to encode local maximum, minimum and center patterns into a three-bit binary code, named MMC pattern. Finally, a compact multi-pattern encoding descriptor is proposed by combining the traditional local sign pattern and MMC pattern. Experimental results on five representative texture databases demonstrate that our method achieves the state-of-the-art texture classification performance. [ABSTRACT FROM AUTHOR]