State of the art logging selection to explore tight, low contrast, hidden reservoir

Hazy images produce negative influences on visual applications in the open air since they are in poor visibility with low contrast and whitening color. Numerous existing methods tend to derive a totally rough estimate of scene depth. Unlike previous work, we focus on the probability distribution of depth that is considered as a scene prior. Inspired by the denoising work of multiplicative noises, the inverse problem for hazy removal is recast as deriving the optimal difference between scene irradiance and the airlight from a constrained energy functional under Bayesian and variation theories. Logarithmic maximum a posteriori estimator and a mixed regularization term are introduced to formulate the energy functional framework where the regularization parameter is adaptively selected. The airlight, another unknown quantity, is inferred precisely under a geometric constraint and dark channel prior. With these two estimates, scene irradiance can be recovered. The experimental results on a series of hazy images reveal that, in comparison with several relevant and most state-of-the-art approaches, the proposed method outperforms in terms of vivid color and appropriate contrast.

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Efficient Color Correction Using Normalized Singular Value for Duststorm Image Enhancement

J ◽

10.3390/j5010002 ◽

2022 ◽

Vol 5 (1) ◽

pp. 15-34

Author(s):

Ho-Sang Lee

Keyword(s):

State Of The Art ◽

Singular Value ◽

Color Channel ◽

Low Contrast ◽

Color Distortion ◽

Dark Channel ◽

Color Cast ◽

Patch Image ◽

Value Decomposition ◽

Contrast Image

A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color distortion owing to an imbalance in the color channel, which is disturbed by sand particles. As a result, a duststorm image has a degraded color channel, which is rare in certain channels. Therefore, a color balance step is needed to enhance a duststorm image naturally. This study goes through two steps to improve a duststorm image. The first is a color balance step using singular value decomposition (SVD). The singular value shows the image’s diversity features such as contrast. A duststorm image has a distorted color channel and it has a different singular value on each color channel. In a low-contrast image, the singular value is low and vice versa. Therefore, if using the channel’s singular value, the color channels can be balanced. Because the color balanced image has a similar feature to the haze image, a dehazing step is needed to improve the balanced image. In general, the dark channel prior (DCP) is frequently applied in the dehazing step. However, the existing DCP method has a halo effect similar to an over-enhanced image due to a dark channel and a patch image. According to this point, this study proposes to adjustable DCP (ADCP). In the experiment results, the proposed method was superior to state-of-the-art methods both subjectively and objectively.

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State Of The Art In Petrophysical Modeling Of Low Resistive Low Contrast Pays (Lrlc) Using Ideal Resistivity Model

10.3997/2214-4609.201803256 ◽

2018 ◽

Author(s):

H. Hassani

Keyword(s):

State Of The Art ◽

Low Contrast ◽

Petrophysical Modeling ◽

Resistivity Model

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Active Contours Using Harmonic Global Division Function

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.2866 ◽

2019 ◽

Vol 9 (4) ◽

pp. 4457-4462

Author(s):

M. T. Bhatti ◽

S. Soomro ◽

A. M. Bughio ◽

T. A. Soomro ◽

A. Anwar ◽

...

Keyword(s):

Level Set ◽

Active Contours ◽

State Of The Art ◽

Gaussian Kernel ◽

Pressure Force ◽

Harmonic Intensity ◽

Low Contrast ◽

Intensity Information ◽

New Energy ◽

Previous State

This paper presents the region-based active contours method based on the harmonic global signed pressure force (HGSPF) function. The proposed formulation improves the performance of the level set method by utilizing intensity information based on the global division function, which has the ability to segment out regions with higher intensity differences. The new energy utilizes harmonic intensity, which can better preserve the low contrast details and can segment complicated areas easily. A Gaussian kernel is adjusted to regularize level set and to escape an expensive reinitialization. Finally, a set of real and synthetic images are used for validation of the proposed method. Results demonstrate the performance of the proposed method, the accuracy values are compared to previous state-of-the-art methods.

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Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection

Applied Sciences ◽

10.3390/app10238754 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8754

Author(s):

Wajeeha Sultan ◽

Nadeem Anjum ◽

Mark Stansfield ◽

Naeem Ramzan

Keyword(s):

Deep Learning ◽

Object Detection ◽

State Of The Art ◽

Salient Object Detection ◽

Salient Object ◽

Global Level ◽

Low Contrast ◽

Qualitative And Quantitative ◽

Exact Boundary ◽

Quantitative Analyses

Salient-object detection is a fundamental and the most challenging problem in computer vision. This paper focuses on the detection of salient objects, especially in low-contrast images. To this end, a hybrid deep-learning architecture is proposed where features are extracted on both the local and global level. These features are then integrated to extract the exact boundary of the object of interest in an image. Experimentation was performed on five standard datasets, and results were compared with state-of-the-art approaches. Both qualitative and quantitative analyses showed the robustness of the proposed architecture.

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A Multi-Gaussian Fuzzy Membership Function to the Algorithm Fuzzy GrowCut Applied to Segment Lesions in Mammography Images

10.5753/eniac.2018.4426 ◽

2018 ◽

Author(s):

Filipe R. Cordeiro ◽

Beatriz Albuquerque ◽

Valmir Macario

Keyword(s):

Breast Cancer ◽

Membership Function ◽

State Of The Art ◽

Accurate Diagnosis ◽

Fuzzy Membership ◽

Fuzzy Membership Function ◽

Tumor Segmentation ◽

Membership Functions ◽

Low Contrast

Segmentation of masses in mammography images is an important task to aid the accurate diagnosis of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. One of the techniques of state of the art for tumor segmentation is the Fuzzy GrowCut algorithm. In this work a study is performed on the behavior of this algorithm when using different membership functions for segmentation. Moreover, this research proposes a new membership function, called Multi-Gaussian, which improves the results of Fuzzy GrowCut with respect to those obtained through the use of classical functions.

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Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Remote Sensing ◽

10.3390/rs11202372 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2372 ◽

Cited By ~ 1

Author(s):

Junpeng Zhang ◽

Xiuping Jia ◽

Jiankun Hu

Keyword(s):

State Of The Art ◽

Traffic Monitoring ◽

Local Region ◽

Video Frame ◽

Monitoring Systems ◽

Coarse Scale ◽

New Developments ◽

Low Contrast ◽

The Third ◽

Vast Territory

Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.

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Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

Journal of Imaging ◽

10.3390/jimaging6070065 ◽

2020 ◽

Vol 6 (7) ◽

pp. 65 ◽

Cited By ~ 1

Author(s):

Sulaiman Vesal ◽

Andreas Maier ◽

Nishant Ravikumar

Keyword(s):

Cardiac Mri ◽

State Of The Art ◽

Automatic Segmentation ◽

Learning Approaches ◽

Target Structure ◽

Low Contrast ◽

Fully Convolutional Networks ◽

Multi Stage ◽

Density Map ◽

End To End

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.

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A Novel Technique Based on Visual Words Fusion Analysis of Sparse Features for Effective Content-Based Image Retrieval

Mathematical Problems in Engineering ◽

10.1155/2018/2134395 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 23

Author(s):

Muhammad Yousuf ◽

Zahid Mehmood ◽

Hafiz Adnan Habib ◽

Toqeer Mahmood ◽

Tanzila Saba ◽

...

Keyword(s):

Image Retrieval ◽

State Of The Art ◽

The State ◽

Content Based Image Retrieval ◽

Scale Invariant ◽

Low Contrast ◽

Large Rotation ◽

Visual Words ◽

Features Fusion ◽

Novel Technique

Content-based image retrieval (CBIR) is a mechanism that is used to retrieve similar images from an image collection. In this paper, an effective novel technique is introduced to improve the performance of CBIR on the basis of visual words fusion of scale-invariant feature transform (SIFT) and local intensity order pattern (LIOP) descriptors. SIFT performs better on scale changes and on invariant rotations. However, SIFT does not perform better in the case of low contrast and illumination changes within an image, while LIOP performs better in such circumstances. SIFT performs better even at large rotation and scale changes, while LIOP does not perform well in such circumstances. Moreover, SIFT features are invariant to slight distortion as compared to LIOP. The proposed technique is based on the visual words fusion of SIFT and LIOP descriptors which overcomes the aforementioned issues and significantly improves the performance of CBIR. The experimental results of the proposed technique are compared with another proposed novel features fusion technique based on SIFT-LIOP descriptors as well as with the state-of-the-art CBIR techniques. The qualitative and quantitative analysis carried out on three image collections, namely, Corel-A, Corel-B, and Caltech-256, demonstrate the robustness of the proposed technique based on visual words fusion as compared to features fusion and the state-of-the-art CBIR techniques.

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Metallographic image segmentation of GCr15 bearing steel based on CGAN

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209441 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 1237-1243 ◽

Cited By ~ 1

Author(s):

Yuanyuan Chen ◽

Wuyin Jin ◽

Meng Wang

Keyword(s):

Deep Learning ◽

Carbide Particle ◽

State Of The Art ◽

Bearing Steel ◽

Segmentation Method ◽

Low Contrast ◽

Gcr15 Bearing Steel ◽

Proposed Model ◽

Metallographic Images ◽

Particle Segmentation

A novel deep learning segmentation method based on Conditional Generative Adversarial Nets (CGAN) is proposed, being U-GAN in this paper to overtake shortcomings of the metallographic images of GCr15 bearing steel, such as multi-noise, low contrast and difficult to segment. The results of experiment indicate that the proposed model is the most accurate comparing with the digital image processing methods and deep learning methods on carbide particle segmentation. The average Dice’s coefficient of similarity measure function is 0.9158, which is the state-of-the-art performance on dataset.

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