S. K. B. Sangeetha (19173255), M. Sivakumar (6414938), Sandeep Kumar Mathivanan (17095304) et al. · Figshare · 2024
<div><p>In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images. The method combines semantic and spatial data using cGAN framework to produce realistic urban scenes while maintaining crucial details. To assess the performance of the proposed method, extensive experiments are performed on benchmark datasets, the ISPRS Potsdam and Vaihingen datasets. Intersection over Union (IoU) and Pixel Accuracy are two quantitative metrics used to evaluate the segmentation accuracy of the produced maps. The proposed method outperforms traditional methods with an IoU of 87% and a Pixel Accuracy of 93%. The experimental findings show that the suggested cGAN-based method performs better than traditional techniques, attaining better segmentation accuracy and generating better urban maps with finely detailed information. The suggested approach provides a framework for resolving the image-to-image translation difficulties in urban scene segmentation, demonstrating the potential of cGANs for producing excellent urban maps from satellite data.</p></div>
Cited by 0Open access
Manqi Li (22633830), Lili Ren (174380) · Figshare · 2025
Republished from images.cv, Rhinoceros Image Classification Dataset under a CC BY 4.0 license, with permission from images.cv, original copyright 2020.
Cited by 0Open access
John D. Austin, Stephen M. Pizer, John D. Austin I et al. · 1987
Cited by 0
Karel J. Zuiderveld · Elsevier eBooks · 1994
Cited by 3,796
JA Stark, William J. Fitzgerald · Signal Processing · 1994
Cited by 11
Wurood A. Jbara, Rafah Amer Jaafar · International Journal of Computer Trends and Technology · 2017
Cited by 5
Manas Sarkar, Ardhendu Mandal · Franklin Open · 2023
Medical imaging enables specialists to make diagnoses our internal body organs for detecting anomalies without being intrusive. Unfortunately, the images produced by various modalities are not only plagued by noises, but the images contrasts are also subpar, making it impossible to use them for a delicate task like diagnosis, which is directly concerned with our health. Pre-processing the image becomes obligatory to ameliorate its medical worth and contrast enhancement is a sacrosanct part of diagnostic image pre-processing. Even though some popular contrast enhancement algorithms, such as Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), etc., are widely used, they are insufficient for some medical imaging modalities, such as ultrasound imaging (USG). This paper, named as Selective Apex Adaptive Histogram Equalization (SLAAHE), proposes an effective and efficient contrast enhancement algorithm that dynamically selects the best window size for dividing the low contrast image into image grids and clip limit for clipping the histogram of the low contrast sub-image only. Unique feature of the proposed algorithm is that it clips the image histogram only when it is not well-distributed and ennobles the output image contrast. This makes the algorithm best concerning the contrast enhancement quality, and reduced time and space complexity. Both theoretical analysis and assessment of experimental results with statistical metrics like Universal Image Quality Index (UIQI), Absolute Mean Brightness Error (AMBE), Multi-Scale Structural Similarity Index Measure (MSSSIM), Entropy, and Root Mean Square Error (RMSE) exhibit how the proposed method has gained momentous improvement over existing state-of-the-art methods. For the evaluation metrics UIQI, AMBE, MSSSIM, Entropy, and RMSE, the suggested technique received average scores of 0.79, 0.12, 0.88, 0.92, and 11.52 respectively.
Cited by 12Open access
Fathan Mustaghfirin, Erwin Erwin, Hadrians Kesuma Putra et al. · Journal of Physics Conference Series · 2019
This paper presents a comparison between two image improvement techniques, Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE). Canny edge detection is used as a comparison. The HE method is a contrast enhancement method that is designed to be widely applied and has effectiveness in image improvement that will be carried out by segmentation, while AHE is more effective to be applied to images that aim to recognize patterns. Performance measurement using a peak signal to noise ratio (PNSR) produces an average value of 16.76 for the HE method and for the AHE method for 16.95. Before the edge detection process, the image of the iris is done by the compression stage using discrete wavelet transform. Average compression ratio for all tested iris datasets is 1.27
Cited by 5Open access
Dhanalaxmi Banavath, Paka Sahasra, Malothu Thirumaladevi et al. · AIP conference proceedings · 2025
Cited by 1
Eric Wharton A, Karen Panetta A, Sos Agaian B · 2010
Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. However, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform illumination, and images with improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods which treat the images globally.
Cited by 0
Surya Atmaja, Don Bosco · Universitas Sanata Dharma Repository (Universitas Sanata Dharma) · 2013
Cited by 0Open access
Guna Semjonova, S. Prem Kumar · AIP conference proceedings · 2025
Cited by 0
Na Xin Peng, Yuqiang Chen · Advanced materials research · 2013
Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.
Cited by 1
Kvapil, Jiří · Brno University of Technology Digital Library (Brno University of Technology) · 2009
Diplomová práce je zaměřena na metodu ekvalizace histogramu a její rozšíření o adaptivní okolí. Práce obsahuje popis základních pojmů, na kterých je metoda ekvalizace a adaptivní ekvalizace postavena. Dále se zabývá vlastnostmi lidského vidění a principy jeho napodobení. Praktická část této práce se věnuje zhotovením software, který umožňuje tyto metody použít a na závěr předkládá výsledky, kterých bylo dosaženo.
Cited by 0
Xianfang Sun, Paul L. Rosin, R. R. Martin et al. · IEEE Transactions on Visualization and Computer Graphics · 2009
An algorithm is presented to automatically generate bas-reliefs based on adaptive histogram equalization (AHE), starting from an input height field. A mesh model may alternatively be provided, in which case a height field is first created via orthogonal or perspective projection. The height field is regularly gridded and treated as an image, enabling a modified AHE method to be used to generate a bas-relief with a user-chosen height range. We modify the original image-contrast-enhancement AHE method to use gradient weights also to enhance the shape features of the bas-relief. To effectively compress the height field, we limit the height-dependent scaling factors used to compute relative height variations in the output from height variations in the input; this prevents any height differences from having too great effect. Results of AHE over different neighborhood sizes are averaged to preserve information at different scales in the resulting bas-relief. Compared to previous approaches, the proposed algorithm is simple and yet largely preserves original shape features. Experiments show that our results are, in general, comparable to and in some cases better than the best previously published methods.
Cited by 79
JA Stark, William J. Fitzgerald · Graphical Models and Image Processing · 1996
Cited by 64
Subhasmita Sahoo, Jagyanseni Panda, Mihir Narayan Mohanty · Communications in computer and information science · 2016
Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doesn’t control degree of enhancement of the image. To overcome this drawback, another variant of Histogram Equalization method namely Adaptive Bi-histogram Equalization using Threshold (ABHET) is being proposed. The proposed method undergoes three steps, such as: Histogram segmentation using threshold, Clipping of histogram using mean value of occupied intensity and histogram equalization of each sub-images. Finally all the sub-images are combined into one complete image. Simulation results show that ABHET outperforms other existing HE-based methods and different image quality measures such as: Peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Structural Similarity Index (SSIM) are being used to test the robustness of the proposed method in terms of enhancement of contrast and preservation of brightness.
Cited by 1Open access
Jeffrey Scott Yakoubian · SMARTech Repository (Georgia Institute of Technology) · 1993
Cited by 0
Faikah Zakaria, Hamizah Binti Emran · 2020
A lower extremity stress fracture is account about 80%-90% of all stress fractures and are standard among the person who involved in strength and substantial loading activities. Early detection and diagnosis manage to prevent further severe complication. However, initial plain radiography is unable to identify the presence of stress fracture approximately for an about up to six to ten weeks after the onset of the fracture; especially in the early stages without displacement of the bone fragment. The purpose of this paper is to discuss the enhancement of stress fracture detection of AP ankle digital X-ray by using image enhancement techniques. Two techniques are proposed in this paper, which is Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). These study data images were retrieved from the online database. A total of 10 original images were retrieved, and 10 test images for each Set A and B are produced. The quantitative evaluation of this study was done, which is the structural similarity index measurement (SSIM) and peak noise to signal ratio (PSNR) in MATLAB software. AHE showed that it has better image enhancement and high image quality for SSIM and PSNR value compared to CLAHE. At p>0.05, there is a significant difference between SSIM and PSNR in AHE and CLAHE. From this study, it shows that the image enhancements techniques can be done as pre-processing steps to improve the enhancement of the stress fracture detection in digital AP ankle X-ray images.
Cited by 1
E. J. Wharton, Karen Panetta, Sos С. Agaian · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Image enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task when attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. However, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. In this paper, we introduce new human vision model based automatic image enhancement techniques, multi-histogram equalization as well as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented showing that the proposed algorithms outperform the other algorithms in two important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as well as images with uniform and non-uniform illumination, and images with improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods which treat the images globally.
Cited by 6