scholarly journals Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

2018 ◽  
Vol 7 ◽  
Author(s):  
Yuma Kinoshita ◽  
Hitoshi Kiya
Keyword(s):  
Image Segmentation ◽  
Image Enhancement ◽  
State Of The Art ◽  
Compensation Method ◽  
Parameter Setting ◽  
Segmentation Method ◽  
Single Image ◽  
Exposure Fusion ◽  
Comparison Results ◽  
Enhancement Method

In this paper, an automatic exposure compensation method is proposed for image enhancement. For the exposure compensation, a novel image segmentation method based on luminance distribution is also proposed. Most single-image-enhancement methods often cause details to be lost in bright areas in images or cannot sufficiently enhance contrasts in dark regions. The image-enhancement method that uses the proposed compensation method enables us to produce high-quality images which well represent both bright and dark areas by fusing pseudo multi-exposure images generated from a single image. Here, pseudo multi-exposure images are automatically generated by the proposed exposure compensation method. To generate effective pseudo multi-exposure images, the proposed segmentation method is utilized for automatic parameter setting in the compensation method. In experiments, image enhancement with the proposed compensation method outperforms state-of-the-art image enhancement methods including Retinex-based methods, in terms of both entropy and statistical naturalness. Moreover, visual comparison results show that the proposed compensation method is effective in producing images that clearly present both bright and dark areas.

scholarly journals A Multiscale CNN-CRF Framework for Environmental Microorganism Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Jinghua Zhang ◽  
Chen Li ◽  
Frank Kulwa ◽  
Xin Zhao ◽  
Changhao Sun ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Segmentation Method ◽  
Recall Accuracy ◽  
Evaluation Indexes ◽  
Segmentation Quality ◽  
Segmentation Approach ◽  
Overall Evaluation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

scholarly journals Contrast and Synthetic Multiexposure Fusion for Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Synthetic Image ◽  
Improve Image Quality ◽  
Exposure Fusion ◽  
Novel Technique ◽  
Lighting Conditions

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

scholarly journals Skin Lesion Segmentation using Deep Hypercolumn Descriptors

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Dhanesh Ramachandram ◽  
Graham W. Taylor
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
State Of The Art ◽  
Jaccard Index ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Malignant Skin

We present a image segmentation method based on deep hypercolumndescriptors which produces state-of-the-art results for thesegmentation of several classes of benign and malignant skin lesions.We achieve a Jaccard index of 0.792 on the 2017 ISIC SkinLesion Segmentation Challenge dataset.

scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

Medical Image Segmentation Algorithm Based on Granular Computing

2012 ◽  
Vol 532-533 ◽  
pp. 1578-1582
Author(s):  
Fang Wang ◽  
Juan Juan Ruan ◽  
Gang Xie
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Granular Computing ◽  
Research Direction ◽  
Medical Image Segmentation ◽  
Image Feature ◽  
Segmentation Method ◽  
Single Image ◽  
Quotient Spaces ◽  
Image Segmentation Algorithm

Granular Computing theory is a interesting research direction in artificial intelligence field. In this paper, granular computing theory is applied to medical image segmentation. Granularity thinking in image segmentation is expounded, and a novel medical image segmentation method is proposed. Firstly, we construct different granularities according to different features that the image contained, secondly, do the attributes combination to the obtained quotient spaces according to the quotient space granularity synthesis principle, and then complete the image segmentation. Compared with the methods adopting single image feature, this method may fully use the image information in a more effective way and may obtain better segmentation effects.

A Fingerprint Image Segmentation Method Based on Fractal Dimension

2012 ◽  
Vol 461 ◽  
pp. 299-301
Author(s):  
Ming Xiang ◽  
Zhen Dong Cui ◽  
Yuan Hong Wu
Keyword(s):  
Image Segmentation ◽  
Fractal Dimension ◽  
Fractal Analysis ◽  
Estimation Algorithm ◽  
Segmentation Method ◽  
Fingerprint Image ◽  
Box Counting ◽  
Comparison Results

Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods

2021 ◽  
Vol 5 (6) ◽  
pp. 1062-1069
Author(s):  
Shoffan Saifullah ◽  
Andiko Putro Suryotomo ◽  
Yuhefizar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Detection Methods ◽  
Adaptive Histogram Equalization ◽  
Chicken Egg ◽  
Enhancement Method ◽  
Edge Detection Method

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

2012 ◽  
Vol 490-495 ◽  
pp. 1251-1255 ◽  
Author(s):  
Hong Cai ◽  
Xue Yuan Zhang ◽  
Hai Tao Dai ◽  
Dong Ming Zhou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Adaptive Algorithm ◽  
Experimental Results ◽  
Edge Extraction ◽  
Segmentation Method ◽  
Adaptive Parameters ◽  
Selection Of ◽  
Better Than

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

scholarly journals Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaoqin Wei ◽  
Xiaowen Chen ◽  
Ce Lai ◽  
Yuanzhong Zhu ◽  
Hanfeng Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Disease Diagnosis ◽  
Superior Performance ◽  
Generative Adversarial Networks ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Adversarial Networks ◽  
Liver Image

Liver image segmentation has been increasingly employed for key medical purposes, including liver functional assessment, disease diagnosis, and treatment. In this work, we introduce a liver image segmentation method based on generative adversarial networks (GANs) and mask region-based convolutional neural networks (Mask R-CNN). Firstly, since most resulting images have noisy features, we further explored the combination of Mask R-CNN and GANs in order to enhance the pixel-wise classification. Secondly, k -means clustering was used to lock the image aspect ratio, in order to get more essential anchors which can help boost the segmentation performance. Finally, we proposed a GAN Mask R-CNN algorithm which achieved superior performance in comparison with the conventional Mask R-CNN, Mask-CNN, and k -means algorithms in terms of the Dice similarity coefficient (DSC) and the MICCAI metrics. The proposed algorithm also achieved superior performance in comparison with ten state-of-the-art algorithms in terms of six Boolean indicators. We hope that our work can be effectively used to optimize the segmentation and classification of liver anomalies.

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