Image Enhancement Algorithm Based on Depth Difference and Illumination Adjustment
TL;DR: In this paper, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring.
Abstract: In order to improve the clarity and color fidelity of traffic images under the complex environment of haze and uneven illumination and promote road traffic safety monitoring, a traffic image enhancement model based on illumination adjustment and depth of field difference is proposed. The algorithm is based on Retinex theory, uses dark channel principle to obtain image depth of the field, and uses spectral clustering algorithm to cluster image depth. After the subimages are divided, the local haze concentration is estimated according to the depth of field and the subimages are adaptively enhanced and fused. In addition, the illumination component is obtained by multiscale guided filtering to maintain the edge characteristics of the image, and the uneven illumination problem is solved by adjusting the curve function. The experimental results show that the proposed model can effectively enhance the uneven illumination and haze weather image in the traffic scene and the visual effect of the images is good. The generated image has rich details, improves the quality of traffic images, and can meet the needs of traffic practical application.
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TL;DR: In this article , an ensembled spatial method for image enhancement was proposed, which employed the Laplacian filter, which highlights the areas of fast intensity variation, and then the gradient of the image was determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing.
Abstract: 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.
4 citations
TL;DR: The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings.
Abstract: Today, new media technology has widely penetrated art forms such as film and television, which has changed the way of visual expression in the new media environment. To better solve the problems of weak immersion, poor interaction, and low degree of simulation, the present work uses deep learning technology and virtual reality (VR) technology to optimize the film playing effect. Firstly, the optimized extremum median filter algorithm is used to optimize the “burr” phenomenon and a low compression ratio of the single video image. Secondly, the Generative Adversarial Network (GAN) in deep learning technology is used to enhance the data of the single video image. Finally, the decision tree algorithm and hierarchical clustering algorithm are used for the color enhancement of VR images. The experimental results show that the contrast of a single-frame image optimized by this system is 4.21, the entropy is 8.66, and the noise ratio is 145.1, which shows that this method can effectively adjust the contrast parameters to prevent the loss of details and reduce the dazzling intensity. The quality and diversity of the specific types of images generated by the proposed GAN are improved compared with the current mainstream GAN method with supervision, which is in line with the subjective evaluation results of human beings. The Frechet Inception Distance value is also significantly improved compared with Self-Attention Generative Adversarial Network. It shows that the sample generated by the proposed method has precise details and rich texture features. The proposed scheme provides a reference for optimizing the interactivity, immersion, and simulation of VR film.
3 citations
14 Dec 2022
TL;DR: In this paper , a Super Resolution GAN (SRGAN) is used to super resolute the fine textures of the image by upscaling it and in order to enhance the images further, ESRGAN is used.
Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.
1 citations
14 Dec 2022
TL;DR: In this article , a Super Resolution GAN (SRGAN) is used to super resolute the fine textures of the image by upscaling it and in order to enhance the images further, ESRGAN is used.
Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.
TL;DR: In this paper , the authors presented an approach for the segmentation and classification of brain tumors using Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning.
Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.
References
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TL;DR: A selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering.
Abstract: It is important to enhance the contrast and remove the speckle noise for optical coherence tomography (OCT) images. In this paper, we propose a selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering. In the proposed selective retinex enhancement method, we first calculate the feature image of the original image, which includes the mean value and standard deviation of each pixel in the original image and its correlation image. Second, by applying the FCM clustering algorithm to the feature image, a mask is generated that can distinguish the structure part from the background part in the OCT image. Then, the mask is applied to the multi-scale retinex algorithm, and only the structure part in the OCT image is enhanced. Moreover, the BM3D method is applied to filter the enhanced image. Experimental results demonstrate that the proposed method performs impressively in improving the contrast and removing the speckle noise of OCT images, and it provides better quantitative performance in terms of signal-to-noise ratio, contrast-to-noise ratio, equivalent number of looks, and the edge preservation parameter $ \beta $β.
16 citations
01 Oct 2019
TL;DR: This paper basically intends to enhance the quality of a image using the Histogram Equalization technique on MATLAB (R2014a) software and the outcome of the image parameters before and after applying Histograms Equalization are discussed.
Abstract: Image Enhancement is a method that converts a poor quality image into a image of superior quality. This paper basically intends to enhance the quality of a image using the Histogram Equalization technique on MATLAB (R2014a) software and the outcome of the image parameters before and after applying Histogram Equalization are discussed. The statistical parameters of the image histogram such as mean and median can be used analyse and show an image of improvised image quality. The method is used to improve the image quality of an X-Ray image of a human bone. This paper also discusses the modern and enhanced techniques of Histogram Equalization and their merits over the traditional Histogram Equalization technique.
14 citations
13 citations
TL;DR: A bipartite graph representation along with a sequential singular value decomposition and mini-batch K-means for unsupervised classification of hyperspectral imagery and results show significant improvements made by the proposed algorithm compared to the state-of-art clustering algorithms.
12 citations