scholarly journals An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Muhammad Hameed Siddiqi ◽  
Amjad Alsirhani
Keyword(s):  
Image Enhancement ◽  
Intensity Variation ◽  
Low Contrast ◽  
Naked Eye ◽  
Enhancement Method ◽  
Laplacian Filter ◽  
Spatial Method ◽  
The Difference ◽  
Averaging Filter ◽  
Spatial Enhancement

Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.

scholarly journals Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

2021 ◽  
Vol 9 (2) ◽  
pp. 225
Author(s):  
Farong Gao ◽  
Kai Wang ◽  
Zhangyi Yang ◽  
Yejian Wang ◽  
Qizhong Zhang
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Fusion Method ◽  
Formation Model ◽  
Low Contrast ◽  
White Balance ◽  
Multi Scale ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

Digital Image Enhancement Techniques for Dental Radiographs

2019 ◽  
pp. 1-39 ◽  
Author(s):  
E. Priya
Keyword(s):  
Spatial Frequency ◽  
Image Enhancement ◽  
Additional Treatment ◽  
Diagnostic Purpose ◽  
Low Contrast ◽  
The Public ◽  
Significant Step ◽  
Enhancement Method ◽  
Dental Radiographs ◽  
Digital Image Enhancement

Dental radiographs suffer frequently from issues such as low contrast and non-uniform illumination. The first step, indeed a significant step, is to enhance these digital images to prepare them for successful post-processing. This pre-processing stage assists to increase the contrast between the foreground that is the teeth and bone from the background regions. In this chapter, image enhancement methods based on spatial, frequency, and spatial-frequency are implemented. The dental radiographs used are available in the public database. The performance of the enhancement methods is validated using qualitative and quantitative measures. It is observed from the results that the enhancement method aids in improving dental features such as crowns, fillings, and bridges. This enables human identification and diagnostic purpose in the way it is possible to identify a variety of diseases. It also prevents the need for a remake, saving the patient from an additional treatment.

scholarly journals Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

2021 ◽  
Vol 2066 (1) ◽  
pp. 012050
Author(s):  
Hao Chen ◽  
Hongsen He ◽  
Xinghua Feng
Keyword(s):  
Image Enhancement ◽  
Color Correction ◽  
Dark Channel Prior ◽  
Low Contrast ◽  
Underwater Environment ◽  
Underwater Image ◽  
Blue Channel ◽  
Dark Channel ◽  
Green Background ◽  
Enhancement Method

Abstract Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

scholarly journals Underwater image enhancement algorithm based on dark channel prior and underwater imaging model

2021 ◽  
Vol 336 ◽  
pp. 06033
Author(s):  
Zhengping Sun ◽  
Fubing Li ◽  
Yuying Yang
Keyword(s):  
Image Enhancement ◽  
Dark Channel Prior ◽  
Underwater Imaging ◽  
Low Contrast ◽  
Underwater Environment ◽  
Imaging Model ◽  
Underwater Image ◽  
Dark Channel ◽  
Color Compensation ◽  
Enhancement Method

The main reason for the degradation of the underwater image is the light absorption and scattering. The images are captured in the underwater environment often have some problems such as loss of image information, low contrast, and color distortion. In order to solve the above problems, this paper proposes an image enhancement method for the underwater environment. With the help of the underwater imaging model and dark channel prior theory, a new idea of adding transmission correction and color compensation to G and B color channels is proposed. Experimental results show that, compared with the traditional methods, this method has a better effect on the underwater image with less color deviation.

Image Enhancement Based on Fractional Differential and Image Entropy

2010 ◽  
Vol 159 ◽  
pp. 232-235
Author(s):  
Ya Wei Liu ◽  
Jian Wei Li
Keyword(s):  
Mutual Information ◽  
Image Enhancement ◽  
Experimental Results ◽  
Fractional Operator ◽  
Selection Differential ◽  
Image Entropy ◽  
Enhancement Method ◽  
Fractional Differential ◽  
The Difference

In this paper, a new image enhancement method is proposed based on fractional differential, which can select the differential order automatically by the difference of mutual information (DMI). DMI describes the increase of mutual information in original and enhancement image. Being a measure of ascertaining the ehancement effect, it is considered getting the information balance in the images processed by different differential order. According to it, a criterion of selection differential order is put forward. Image convolutions are implemented with fractional operator in different scales, and then DMI of adjacent scales are calculated. The differential order can be selected in which the DMI is the mininum. The experimental results indicate that the proposed method is effective, and has better result compared with other methods.

scholarly journals Weak-Light Image Enhancement Method Based on Adaptive Local Gamma Transform and Color Compensation

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wencheng Wang ◽  
Xiaohui Yuan ◽  
Zhenxue Chen ◽  
XiaoJin Wu ◽  
Zairui Gao
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Space ◽  
Correction Method ◽  
Source Image ◽  
Low Contrast ◽  
Reflection Model ◽  
Color Compensation ◽  
Enhancement Method ◽  
Weak Light

In weak-light environments, images suffer from low contrast and the loss of details. Traditional image enhancement models are usually failure to avoid the issue of overenhancement. In this paper, a simple and novel correction method is proposed based on an adaptive local gamma transformation and color compensation, which is inspired by the illumination reflection model. Our proposed method converts the source image into YUV color space, and the Y component is estimated with a fast guided filter. The local gamma transform function is used to improve the brightness of the image by adaptively adjusting the parameters. Finally, the dynamic range of the image is optimized by a color compensation mechanism and a linear stretching strategy. By comparing with the state-of-the-art algorithms, it is demonstrated that the proposed method adaptively reduces the influence of uneven illumination to avoid overenhancement and improve the visual effect of low-light images.

An Image Enhancement Method in Mixed Space

2013 ◽  
Vol 433-435 ◽  
pp. 400-404
Author(s):  
Qun Li Li ◽  
Li Sha Li ◽  
Feng Xiao
Keyword(s):  
Image Enhancement ◽  
Laplace Operator ◽  
Gradient Method ◽  
Enhancement Effect ◽  
Laplace Method ◽  
Sobel Operator ◽  
Enhancement Method ◽  
Spatial Method ◽  
The Laplace Operator ◽  
Better Than

An image enhancement method in mixed space is proposed in this paper, it combines the Laplace operator and the Sobel operator, gives full play the advantages of the two algorithms. It shows that the image enhancement effect of the mixed spatial method is better than the Laplace method and gradient method, through the enhancement experiments to the same image in the three ways, comparing and analyzing the results of their treatment.

scholarly journals Exploring Image Enhancement for Salient Object Detection in Low Light Images

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-19
Author(s):  
Xin Xu ◽  
Shiqin Wang ◽  
Zheng Wang ◽  
Xiaolong Zhang ◽  
Ruimin Hu
Keyword(s):  
Object Detection ◽  
Image Enhancement ◽  
Ground Truth ◽  
Salient Object Detection ◽  
Salient Object ◽  
Low Light ◽  
Low Contrast ◽  
Local Content ◽  
The Difference ◽  
Public Datasets

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

Sign in / Sign up

Export Citation Format

Share Document