Image texture surface analysis using an improved differential box counting based fractal dimension

Image-based dust quantification: a novel approach using texture and color features

Airborne dust pollution, particularly in the mining environment, poses significant environmental and health risks due to its fine particulate matter. Traditional monitoring methods, while widely used, face limitations in real-time detection and spatial accuracy. In this study, an image-based methodology is presented for quantifying dust concentration, integrating texture and color features to enhance detection precision. Using a self-designed experimental setup replicating underground mining conditions, the high-resolution images of dust dispersion are acquired under controlled conditions and extract feature parameters, including Entropy in texture and Standard Deviation in color features, across multiple color models. The approach demonstrates a strong correlation between image features and dust concentration, achieving an R2 of 0.90 with expanded measurement ranges. A comparative analysis against existing laser-based and vision-based techniques highlights the method accuracy and computational efficiency, suggesting its potential for real-time, high-precision dust monitoring. This work establishes a foundation for advanced image-based dust quantification, with applications extending to diverse industrial monitoring scenarios.

Facile fabrication of Au-Ag alloy nanoparticles/Ag nanowires SERS substrates with bimetallic synergistic effect for ultra-sensitive detection of crystal violet and alkali blue 6B

The bimetallic nanostructure of Au and Ag can integrate two distinct properties into a novel substrate compared to single metal nanostructures. This work presents a rapid and sensitive surface-enhanced Raman scattering (SERS) substrate for detecting illegal food additives and dyes of crystal violet (CV) and alkali blue 6B (AB 6B). Au-Ag alloy nanoparticles/Ag nanowires (Au-Ag ANPs/Ag NWs) were prepared by solid-state ionics method and vacuum thermal evaporation method at 5μA direct current electric field (DCEF), the molar ratio of Au to Ag was 1:18.34. Many 40 nm-140 nm nanoparticles regularly existed on the surface of Ag NWs with the diameters from 80 nm to 150 nm. The fractal dimension of Au-Ag ANPs/Ag NWs is 1.69 due to macroscopic dendritic structures. Compared with single Ag NWs, the prepared Au-Ag ANPs/Ag NWs substrates show superior SERS performance because of higher surface roughness, the SERS active of Ag NWs and bimetallic synergistic effect caused by Au-Ag ANPs, so the limit of detections (LOD) of Au-Ag ANPs/Ag NWs SERS substrates toward detection of CV and AB 6B were as low as 10-16mol/L and 10-9mol/L, respectively. These results indicate that Au-Ag ANPs/Ag NWs substrates can be used for rapid and sensitive detection of CV and AB 6B and have great development potential for detection of illegal food additives and hazardous substances in the fields of environmental monitoring and food safety.

Fractal Analysis of Fuel Nozzle Surface Morphology Based on the 3D-Sandbox Method

The dual oil circuit centrifugal fuel nozzle is made of martensitic stainless steel, which has complex morphological characteristics. The surface roughness characteristics of the fuel nozzle directly affect the degree of fuel atomization and the spray cone angle. The surface characterization of the fuel nozzle is investigated by the fractal analysis method. A sequence of images of an unheated treatment fuel nozzle and a heated treatment fuel nozzle are captured by the super-depth digital camera. The 3-D point cloud of the fuel nozzle is acquired by the shape from focus technique, and its three-dimensional (3-D) fractal dimensions are calculated and analyzed by the 3-D sandbox counting method. The proposed method can characterize the surface morphology well, including the standard metal processing surface and the fuel nozzle surface, and the experiments show that the 3-D surface fractal dimension is positively correlated with the surface roughness parameter. The 3-D surface fractal dimensions of the unheated treatment fuel nozzle were 2.6281, 2.8697, and 2.7620, compared with the heated treatment fuel nozzles dimensions of 2.3021, 2.5322, and 2.3327. Thus, the 3-D surface fractal dimension value of the unheated treatment is larger than that of the heated treatment and is sensitive to surface defects. This study indicates that the 3-D sandbox counting fractal dimension method is an effective method to evaluate the fuel nozzle surface and other metal processing surfaces.

Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique

Here, we propose a CNN-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. The proposed algorithm consists of the following three steps. First, target infrared features based on a real infrared image are extracted through pretrained VGG-19 networks. Next, by implementing a neural style-transfer algorithm to a simulated infrared image, fractal nature features from the real infrared image are progressively applied to the image. Therefore, the fractal characteristics of the simulated image are improved. Finally, based on the results of fractal analysis, peak signal-to-noise (PSNR), structural similarity index measure (SSIM), and natural image quality evaluator (NIQE) texture evaluations are performed to know how the simulated infrared image is properly transformed as it contains the real infrared fractal features. We verified the proposed methodology using a simulation with three different simulation conditions with a real mid-wave infrared (MWIR) image. As a result, the enhanced simulated infrared images based on the proposed algorithm have better NIQE and SSIM score values in both brightness and fractal characteristics, indicating the closest similarity to the given actual infrared image. The proposed image fractal feature analysis technique can be widely used not only for the simulated infrared images but also for general synthetic images.

Urban spatial form analysis based on the architectural layout -- Taking Zhengzhou City as an example

The analysis of urban spatial form is the basic research of urban development. Traditional fractal research often focuses on the urban spatial layout, which cannot visually express the specific form, change characteristics and development trend of urban architectural spaces.The urban architectural form is simplified and the basic architectural form templates are extracted, and then, the correlations between architecture form and fractal dimension are built. The results of the case study show that the architectural layout of Zhengzhou City exhibits obvious fractal characteristics, and the combination of the two-dimensional and three-dimensional fractal dimensions is helpful for comprehensively revealing the architectural layout information. Moreover, the fractal dimension of buildings shows that the gradient from the inner to outer ring decreases, similar to the 'annual growth rings' of trees. Obvious differences exist in the fractal dimensions of urban buildings in different directions, reflecting the urban expansion direction. This study promotes the visualization of fractal theory and the expression of fractal theory in spatial gradient, providing theoretical and data reference for urban spatial form optimization.

Problems of the Grid Size Selection in Differential Box-Counting (DBC) Methods and an Improvement Strategy

The differential box-counting (DBC) method is useful for determining the fractal dimension of grayscale images. It is simple to learn and implement and has been extensively utilized. However, this approach has several problems, such as over- or undercounting the number of boxes due to inappropriate parameter choices, limiting the calculation accuracy. Many studies have been conducted to increase the algorithm’s computational accuracy by improving the calculating parameters of the differential box-counting method. The grid size is a crucial parameter for the DBC method. Generally, there are two typical ways for selecting the grid size in relevant studies: consecutive integer and divisors of image size. However, both methods for grid size selection are problematic. The consecutive integer method cannot partition the image entirely and will result in the undercounting of boxes; the divisors of image size can partition the image completely. However, this method uses fewer grid sizes to compute fractal dimensions and has a relatively huge distance error (DE). To address the shortcomings of the above-mentioned two approaches, this research presents an improved grid size selection strategy. The improved method enhances computational accuracy by computing the discarded image edge areas in the consecutive integer method, allowing the original image information to be used as thoroughly as the divisor strategy. Based on fractional Brownian motion (FBM), Brodatz, and Aerials image sets, the accuracy of the three grid size selection techniques (consecutive integer method, divisors of image size method, and the improved algorithm) to compute the fractal dimension is then compared. The results reveal that, compared to the two prior techniques, the revised algorithm described in this study minimizes the distance error and increases the accuracy of the fractal dimension computation.

Numerical Analysis of the Flow Effect of the Menger-Type Artificial Reefs with Different Void Space Complexity Indices

Based on fractal theory, a regular fractal is used to construct symmetrical reef models (e.g., cube and triangle reef models) with different fractal levels (n = 1, 2, 3). Using the concept of fractal dimension, we can better understand the spatial effectiveness of artificial reefs. The void space complexity index is defined to quantify the complexity of the internal spatial distribution of artificial reefs models under different levels. The computational fluid dynamics (CFD) flow simulation approach was used to investigate the effects of void space complexity on the flow field performances of the symmetrical artificial reef models. The upwelling convection index (Hupwelling/HAR, Vupwelling/VAR), wake recirculating index (Lwake/LAR, Vwake/VAR) and non-dimensionalized velocity ratio range were used to evaluate the efficiency of the flow field effect inside or around artificial reefs. The surface area and spatial complexity index of artificial reefs increase with increasing fractal level. The numerical simulation data shows that the Menger-type artificial reef models with a higher spatial complexity index have better flow field performances in the upwelling and wake regions. Compared to the traditional artificial reef models, the upwelling convection index (Vupwelling/VAR) and recirculating index (Vwake/VAR) of n = 3 fractal cube artificial reef increase by 37.5% and 46.8%, respectively. The efficiency indices of the upwelling region and wake region around the fractal triangle artificial reef model are 2–3 times those of the fractal cube artificial reef model when the fractal level is 3.

Multi-focus image fusion using fractal dimension

Multi-focus image fusion is defined as "the combination of a group of partially focused images of a same scene with the objective of producing a fully focused image." Normally, transform-domain-based image fusion methods preserve the textures and edges in the blend image, but many are translation variant. The translation-invariant transforms produce the same size approximation and detail images, which are more convenient to devise the fusion rules. In this work, a translation-invariant multi-focus image fusion approach using the à-trous wavelet transform is introduced, which uses fractal dimension as a clarity measure for the approximation coefficients and Otsu's threshold to fuse the detail coefficients. The subjective assessment of the proposed method is carried out using the fusion results of nine state-of-the-art methods. On the other hand, eight fusion quality metrics are considered for the objective assessment. The results of subjective and objective assessment on grayscale and color multi-focus image pairs illustrate that the proposed method is competitive and even better than some of the existing methods.