Contrast Enhancement of Microscopy Images Using Image Phase Information

Cakir, Serdar; Kahraman, Deniz; Cetin-Atalay, Rengül; Çetin, A. Enis

doi:10.1109/access.2018.2796646

Cited by 23 publications

(16 citation statements)

References 31 publications

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“…Contrast Enhancement is an important step for the analysis of microscopy images. In paper [50] is proposed groundbrakeing design of the Phase Contrast Microscopy Framework. The proposed image enhnacement framework transforms the changes in image phase into the variations of magnitude to enhance the structural details of the image and to improve visibility.…”

Section: Latest Image Enhancement Techniquesmentioning

confidence: 99%

A Review on Image Enhancement Techniques

2019

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Image Enhancement is one of the most important and complex techniques in image processing technology. The main aim of image enhancement is to improve the visual appearance on an image and to offer a better representation of the image for Computer Vision Algorithms. In this paper,we is covered a few application fields of image enhancement with various images like grayscale, color, infrared and even with videos. The main objective of this paper is to highlight the drawbacks of the state of the art image enhancement techniques.

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Section: Latest Image Enhancement Techniquesmentioning

confidence: 99%

A Review on Image Enhancement Techniques

2019

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“…For better interpretation of those images with limited dynamic range, CE is often performed to bring out unclear details. The technique of image CE has received much attention and been widely used in industrial and medical imagery (e.g., [1]- [5]). Among the methods have been proposed, one popular procedure is histogram equalization (HE) (e.g., [6], [7]).…”

Section: Introductionmentioning

confidence: 99%

Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Mai

Meng

et al. 2019

IEEE Access

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Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches. INDEX TERMS Image enhancement, reversible data hiding, 2D histogram, image quality.

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“…Deterioration of rubber is mainly due to deformation caused by molecular cleavage, external force, and heat. In MI of rubber materials, it is required to identify the cause of deterioration by utilizing information on the degradation of stored rubber materials, and it is expected that new knowledge for the development of highly durable rubber materials will be obtained [27].…”

Section: Introductionmentioning

confidence: 99%

Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model

et al. 2019

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A method for estimating regions of deterioration in electron microscope images of rubber materials is presented in this paper. Deterioration of rubber materials is caused by molecular cleavage, external force, and heat. An understanding of these characteristics is essential in the field of material science for the development of durable rubber materials. Rubber material deterioration can be observed by using on electron microscope but it requires much effort and specialized knowledge to find regions of deterioration. In this paper, we propose an automated deterioration region estimation method based on deep learning and anomaly detection techniques to support such material development. Our anomaly detection model, called Transfer Learning-based Deep Autoencoding Gaussian Mixture Model (TL-DAGMM), uses only normal regions for training since obtaining training data for regions of deterioration is difficult. TL-DAGMM makes use of extracted high representation features from a pre-trained deep learning model and can automatically learn the characteristics of normal rubber material regions. Regions of deterioration are estimated at the pixel level by calculated anomaly scores. Experiments on real rubber material electron microscope images demonstrated the effectiveness of our model. INDEX TERMS Materials informatics, anomaly detection, deep learning, transfer learning.

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Contrast Enhancement of Microscopy Images Using Image Phase Information

Cited by 23 publications

References 31 publications

A Review on Image Enhancement Techniques

A Review on Image Enhancement Techniques

Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model

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