Detail Layer Research Articles

Distribution of 50-layer corneal densitometry values and related factors.

To investigate the distribution of 50 layers of corneal densitometry and related factors. Clinical data, including age, sex, central corneal thickness, corneal keratometry, and diopters, were collected from 102 healthy participants (102 eyes) in this retrospective study. The cornea was divided into 50 layers, and densitometry of each layer at 19 points was measured by the Pentacam. The value versus the depth curve was plotted. Paired-sample t test and one-way analysis of variance were used to compare densitometry in different regions or depth. Statistical significance was defined as P < .05. The densitometry values of the Bowman membrane (10-14% depth), anterior stroma (14-30% depth), epithelium (0-10% depth), and Descemet membrane (94-98% depth) decreased sequentially, and the densitometry values of the middle and posterior stroma (30-94% depth) and endothelium (98-100% depth) were the lowest. The higher the degree of astigmatism, the higher the second densitometry peak (R = 0.277, P < .001). The densitometry values of the vertex and superior parts of the cornea were higher than those in the periphery and inferior parts, respectively (all P < .001). In the Bowman membrane, the densitometry is lowest in the inferior nasal part, while in the Descemet membrane, it is lowest in the inferior temporal part. Two densitometry peaks appeared near the Bowman membrane and Descemet membrane. For different depths, the distribution of densitometry within a layer is different. We provide a methodological reference and data basis for corneal research based on local changes in densitometry, and help understand the details of corneal structure from an optical perspective through detailed layering and zoning analysis of densitometry.

Poe in Cyberspace: The Pleasure Principle Revisited

Poe in Cyberspace: The Pleasure Principle Revisited

Layering Vibration Transfer Path Analysis of a Flexible Supported Gear System Based on the Vibration Power Flow Theory

In the marine gear system, the vibration of gears is transmitted to the ship foundation through shafts, bearings, housing, and isolators, and then the underwater noise is generated. Analyzing the vibration transfer properties and identifying the critical path can provide guidance to the low-noise design of the gear system. In this paper, a coupled gear-housing-foundation dynamic model is proposed for a flexible supported gear system, in which the flexibility of each subsystem and the coupling relationship between them are taken into account. The transferring process of gear vibration is divided into different layers between gears, shafts, bearings, housing, isolators, and ship foundation. Based on the vibration power flow (VPF) theory, detailed layering vibration transfer path analyses of the gear isolation coupled system are carried out for both location path and direction path, and the main paths are identified in a broad speed range. The results indicate that though the gear vibration transferred to the foundation attenuate layer by layer, there may be reverse VPF in some special location path and direction path under resonance conditions.

Multi-Sensor Medical-Image Fusion Technique Based on Embedding Bilateral Filter in Least Squares and Salient Detection

A multi-sensor medical-image fusion technique, which integrates useful information from different single-modal images of the same tissue and provides a fused image that is more comprehensive and objective than a single-source image, is becoming an increasingly important technique in clinical diagnosis and treatment planning. The salient information in medical images often visually describes the tissue. To effectively embed salient information in the fused image, a multi-sensor medical image fusion method is proposed based on an embedding bilateral filter in least squares and salient detection via a deformed smoothness constraint. First, source images are decomposed into base and detail layers using a bilateral filter in least squares. Then, the detail layers are treated as superpositions of salient regions and background information; a fusion rule for this layer based on the deformed smoothness constraint and guided filtering was designed to successfully conserve the salient structure and detail information of the source images. A base-layer fusion rule based on modified Laplace energy and local energy is proposed to preserve the energy information of these source images. The experimental results demonstrate that the proposed method outperformed nine state-of-the-art methods in both subjective and objective quality assessments on the Harvard Medical School dataset.

An Improved Multiexposure Image Fusion Technique.

Multiexposure image fusion (MEF) is an effective approach to generate high dynamic range images from multilevel exposures taken from ordinary cameras. In this article, a novel MEF algorithm is proposed to gain maximum visual details as well as vivid colors from the captured scene. This algorithm first decomposes the input images with multiple exposures into the base and detail layer. The weights for the base and detail layers are computed by using exposedness function and then both the layers are combined to generate the final fused image. The proposed multiexposure technique requires fewer computational operations, preserves edges, and also reduces spatial artifacts. The proposed technique has been evaluated quantitatively using image quality assessment model based on structure similarity index measure for MEF. By the extensive experimental results, it has been illustrated that in addition to significantly outperforming other state-of-the-art techniques, the proposed technique is much faster and can achieve better image quality.

Multimodal medical image fusion based on visual saliency map and multichannel dynamic threshold neural P systems in sub-window variance filter domain

As a powerful image enhancement technique, multimodal medical image fusion has been widely used for biomedical diagnosis and surgical navigation. However, the trade-off between efficiency and fusion quality for existing fusion images remains a great challenge. In this study, a robust and efficient medical image fusion method based on a sub-window variance filter (SVF) is proposed to overcome the above problems. First, the input images are decomposed two-layer using SVF, the base layer including rich energy and contour intensity information, and the detail layer containing integral detail features. Then, a contrast function is employed to improve the MRI base layer and a neighbor energy function to refine the detail layer. Next, a novel multichannel dynamic threshold neural P system is proposed to fuse the detail layer, which can provide a comprehensive consideration of the information in the detail layer and compensate well for the defect of the single-channel model. Moreover, a visual saliency map (VSM-based) is designed to fuse the base layer, preserving important energy information and improving image contrast. Finally, the fused result is reconstructed by the inverse SVF. Ten representative current multimodal medical image fusion methods are compared, and six typical quality evaluation metrics are combined to objectively evaluate the fused images. Tremendous experimental results indicate that the proposed model can effectively preserve higher contrast, more complete edge features, and better color than some state-of-the-art algorithms, both visual quality and quantitative evaluations on public datasets.

Current Non-Invasive Imaging Techniques Used in the Diagnosis of Adenomyosis

Objective: This review aims to provide insights into the current non-invasive imaging methods used in the diagnosis of adenomyosis, as well as to highlight their diagnostic accuracy, advantages, disadvantages and limitations in the detection of this benign uterine condition. At the same time, this paper emphasizes the importance of using consensus-based terminology in the imaging description of these lesions. Mechanism: A search of PUBMED database was conducted for articles published between January 1998 and August 2022 and studies which compared non-invasive imaging methods with postoperative histology examination of uterine specimens were primarily selected. Transvaginal two-dimensional ultrasound was for a long time the main non-invasive imagistic method used for assessment of adenomyosis lesions. The introduction of Morphological Uterus Sonographic Assessment (MUSA) group criteria yielded a significantly better diagnostic outcome of adenomyosis in case of conventional ultrasonography, but the distinction of concomitant benign uterine conditions still remained challenging. Findings in Brief: The addition of three-dimensional ultrasound or elastosonography to conventional two-dimensional transvaginal ultrasound yielded higher diagnostic sensitivity. Qualitative elastography particularly proved useful in the diagnosis of adenomyosis due to its capacity to achieve differential diagnosis of benign uterine pathologies based on lesion stiffness. Magnetic resonance imaging (MRI) examination presents higher diagnostic specificity and sensitivity, can assess the endometrial and myometrial layers in detail, but its use has been shadowed by costs and significantly longer examination time. Conclusions: The imaging terminology established by consensus by the MUSA group in recent years has facilitated the description of adenomyosis-specific lesions. Two-dimensional transvaginal ultrasound in combination with three-dimensional ultrasound or combined with qualitative elastography offers similar diagnostic sensitivity and specificity to MRI. Despite some limitations, MRI remains a reliable diagnostic method for adenomyosis.

Infrared and visible image fusion with entropy-based adaptive fusion module and mask-guided convolutional neural network

Since infrared and visible images have different descriptions of the same scene and have a lot of complementary information, they are usually fused to obtain a more comprehensive image. To overcome the detail information losing, fusion artifacts, and unnatural fusion in the traditional image fusion, we propose a novel algorithm for infrared and visible image fusion. Firstly, we introduce a guided filter to decompose the input image into a first-level base layer, a second-level base layer and a detail layer. Specifically, for the first-level base layer fusion, we propose an entropy-based image adaptive fusion module, which is guided by the image quality and the information entropy. To reduce the low-frequency information losing, we use the maximum absolute value rule to fuse the second-level base layer. In addition, for the detail layer containing many textures and edges, we construct a mask-guided deep convolutional neural network to fuse it. We label the infrared image pixel-by-pixel in the training set to obtain the foreground target mask, and construct the loss function jointly considering the mask, gradient, and the structural similarity of the image. Masks are only used when training the network. Finally, we reconstruct the image. Experiments show that our algorithm has achieved good performance in both subjective and objective evaluation.

Editorial Issue 34.2

Editorial Issue 34.2

Seamless Texture Optimization for RGB-D Reconstruction.

Restoring high-fidelity textures for 3D reconstructed models are an increasing demand in AR/VR, cultural heritage protection, entertainment, and other relevant fields. Due to geometric errors and camera pose drifting, existing texture mapping algorithms are either plagued by blurring and ghosting or suffer from undesirable visual seams. In this paper, we propose a novel tri-directional similarity texture synthesis method to eliminate the texture inconsistency in RGB-D 3D reconstruction and generate visually realistic texture mapping results. In addition to RGB color information, we incorporate a novel color image texture detail layer serving as an additional context to improve the effectiveness and robustness of the proposed method. First, we select an optimal texture image for each triangle face of the reconstructed model to avoid texture blurring and ghosting. During the selection procedure, the texture details are weighted to avoid generating texture chart partitions across high-frequency areas. Then, we optimize the camera pose of each texture image to align with the reconstructed 3D shape. Next, we propose a tri-directional similarity function to resynthesize the image context within the boundary stripe of texture charts, which can significantly diminish the occurrence of texture seams. Finally, we introduce a global color harmonization method to address the color inconsistency between texture images captured from different viewpoints. The experimental results demonstrate that the proposed method outperforms state-of-the-art texture mapping methods and effectively overcomes texture tearing, blurring, and ghosting artifacts.

Single Image Dehazing based on Additive Wavelet Transform

In this work, a single image dehazing method, which uses the multiscale products of additive wavelet transform as a prior is presented. In this method, first, the additive wavelet transform is applied to the hazy image to obtain its approximation and wavelet layers. Then the multiscale products of the approximation and detail layers of the input hazy image is calculated. The multiscale products of approximation and wavelet layers are summed up to obtain the proposed prior. Observations demonstrate that the proposed prior calculation keeps the detail information of the image, while detecting the haze. Using the proposed prior and commonly used hazy image model together, an efficient dehazing method is constructed. The comparisons between the proposed method and commonly used dehazing methods show that the proposed method has a better dehazing perfomrance than the traditional methods.

Halo-free image enhancement through multi-scale detail sharpening and single-scale contrast stretching

Conventional image enhancement methods manipulate images in just one scale. This is usually not enough in practice since details like edges live across scales. So, enhancing images through multiscale manipulations is necessary to achieve satisfying results. One drawback of conventional multiscale enhancement methods is that halo artifacts are easy to be introduced. In this paper, we propose a novel efficient multi-scale halo-free image enhancement method, which is based on the observation that image enhancement can be decomposed into two layers: detail sharpening and contrast stretching, and multiscale operations need to be employed to process just the detail layer. We then propose to decompose the multiscale detail manipulations into one-dimensional edge-preserving operations such that halos are completely avoided. For the contrast stretching layer, a modified histogram equalization method is proposed, which achieves state-of-the-art global contrast enhancing effect. Numerical experiments show that, compared to existing popular approaches, the proposed method produces quite competitive results for a variety of image types.

Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays.

X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive. In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results. The subjective results indicate that our method achieves higher contrast than the best-performing method (, , ). Furthermore, our method is highly efficient. It takes 0.92 s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images (, , ). We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.

A Target-Aware Fusion Framework for Infrared and Visible Images

Infrared and visible image fusion aims to obtain an image that can retain the prominent infrared target and the detailed texture information from the source images. Most scale filter-based decomposition methods mainly attempt to extract more detailed features by increasing decomposition layers, and they ignore fully considering the intrinsic properties of the original images and the influence of noises. To solve this issue and obtain better fusion & detection performance, this paper proposes a fusion method via a globalet filter and detail enhancement model to construct a target-aware fusion framework. The globalet filter is first comprehensively considered from three perspectives based on global point-to-point optimization: target brightness, removal of gradients of large-small scales (noise reduction), and preservation of texture details. Mathematically, three limited error measure equations are constructed between the target output and the source image in the shape of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> -norm and the first-order derivative difference. Next, a weighted average operator and a detail enhancement model are proposed to guide the corresponding sub-layers. This model creates connections between the detail layers and the input images so that the fused detail layer contains the best pixel region from these images to the greatest extent possible after the “maximum absolute” rule. As a result, the fused image is reconstructed by adding the previously obtained sub-images. Extensive experimental results demonstrate that our method outperforms state-of-the-art fusion methods, particularly in highlighting infrared targets, preserving substantial details, and producing average detection accuracy of exceeds 98%.

Remote Sensing Image Recovery and Enhancement by Joint Blind Denoising and Dehazing

Due to the hazy weather and the long-distance imaging path, the captured remote sensing image (RSI) may suffer from detail loss and noise pollution. However, simply applying dehazing operation on a noisy hazy image may result in noise amplification. Therefore, in this paper, we propose joint blind denoising and dehazing for RSI recovery and enhancement to address this problem. Firstly, we propose an efficient and effective noise level estimation method based on quad-tree subdivision and integrate it into FFDNet for blind denoising. Secondly, a multi-scale guided filter decomposes the denoised hazy image into base and detailed layers, separating the initial details. Then, the dehazing procedure using the corrected boundary constraint is implemented in the base layer, while a nonlinear sigmoid mapping function enhances the detailed layers. The last step is to fuse the enhanced detailed layers and the dehazed base layer to get the final result. Using both synthetic remote sensing hazy image (RSHI) datasets and real-world RSHI, we perform comprehensive experiments to evaluate the proposed method. Results show that our method is superior to well-known methods in both dehazing and joint denoising and dehazing tasks.

Digital watermarking scheme based on curvelet transform and multiple chaotic maps

The rapid development of digital products brings security issues. Digital watermarking technology is an important means to handle these problems. To enhance the imperceptibility of watermark and locate the possible tampering as well, a digital watermarking scheme based on curvelet transform is presented by combining with multiple chaotic maps. The host image is decomposed into three parts, i.e., coarse layer, detail layer and fine layer, with curvelet transform, and a robust watermark is embedded into the coarse layer for copyright protection of digital products. In addition, an authentication watermark is embedded into the fine layer to detect and locate the illegal changes. Simulation results show that the proposed digital watermarking scheme possesses acceptable robustness and security.

Underwater Image Enhancement via Piecewise Color Correction and Dual Prior Optimized Contrast Enhancement

Due to the absorption and scattering of light, underwater captured images often face serious quality degradation issues. In this letter, we propose to cope with the aforementioned issues via piecewise color correction and dual prior optimized contrast enhancement. Specifically, we first present the piecewise color correction method using the maximum mean and two gain factors to correct the color cast of each color channel. Then, we propose a dual prior optimized contrast enhancement method, which relies on the spatial and texture priors to decompose the base layer and detail layer of the V channel in HSV color space. Meanwhile, we employ different enhancement strategies in different layers to enhance the contrast and texture detail of underwater images. Our extensive experiments on several benchmark datasets show that our method outperforms eleven compared state-of-the-art methods. Moreover, our method has good generalization capability for fog and low-light images. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Li-Chongyi/PCDE</uri> .

Tone Mapping Method Based on the Least Squares Method

Tone mapping is used to compress the dynamic range of image data without distortion. To compress the dynamic range of HDR images and prevent halo artifacts, a tone mapping method is proposed based on the least squares method. Our method first uses weights for the estimation of the illumination, and the image detail layer is obtained by the Retinex model. Then, a global tone mapping function with the parameter is used to compress the dynamic range, and the parameter is obtained by fitting the function to the histogram equalization. Finally, the detail layer and the illumination layer are fused to obtain the LDR image. The experimental results show that the proposed method can efficiently restore real-world scene information while preventing halo artifacts. Therefore, tone mapping quality index and mean Weber contrast of the tone-mapped image are 8% and 12% higher than the closest competition tone mapping method.

A Cost-effective Burn-in Compensation Method Using Deep Convolutional Networks with Detail Layer Accumulation

A Cost-effective Burn-in Compensation Method Using Deep Convolutional Networks with Detail Layer Accumulation

The effect of habitat and fishing-effort data resolution on the outcome of seabed status assessment in bottom trawl fisheries

Mobile bottom fishing is widespread and is known to impact seabed habitats. Risk assessments can evaluate these impacts and inform decisions about the certification of sustainable fisheries by bodies like the Marine Stewardship Council. Spatial risk assessments require input data layers of seabed habitats and fishing effort, and when available, they are often available at different resolutions (number of different habitat categories for seabed habitats, spatial cell size for fishing effort). Here we evaluate how the outcome of risk assessments of the impact of bottom trawling depends on resolution of these data layers. We evaluate the relative benthic state, recovery time and the resulting indicative score for the ‘habitats outcome’ of the Marine Stewardship Council (MSC) Fisheries Standard using three different levels of habitat categorisation and four different fishing effort resolutions for 14 areas in the North Sea that are intensely fished by different trawl gears. In this framework outcomes are reported by habitat category. The assessments show that when fishing effort is low, choices about effort and habitat resolution do not affect the scores. Fine and medium resolution habitat maps results in a larger range of seabed status and recovery times than coarse resolution maps, and worse predicted habitat outcomes. In MSC fishery assessments, both mean and minimum scores are important to achieving certification. The mean score for each Principle must be ≥ 80 for a fishery to be certified, with any score below 80 triggering conditions that must be addressed by the fishery within five years of certification. A single score below 60 prevents a fishery being certified. Coarser fishing effort resolution layers lead to higher seabed status and faster recovery times. As a result, more detailed data layers result in a larger range of MSC scores, including more scores that would result in conditions on certification and modifications to fishing practices. In conclusion, for intensely fished areas, more detailed data layers provide a more stringent assessment of the sustainability of bottom trawl fisheries and it is recommended that the most-detailed available fishing effort data and fine or medium scale habitat data is used in assessments.