Relevant bibliographies by topics / Image Sharpening Algorithm

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Image Sharpening Algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Image Sharpening Algorithm"

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Rokni, Komeil. "INVESTIGATING THE IMPACT OF PAN SHARPENING ON THE ACCURACY OF LAND COVER MAPPING IN LANDSAT OLI IMAGERY." Geodesy and cartography 49, no. 1 (2023): 12–18. http://dx.doi.org/10.3846/gac.2023.15308.

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Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infra
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Tasaki, Kuniharu, Tomohisa Nishimura, Taro Hida, Kazushi Maruo, and Tetsuro Oshika. "Effects of Image Processing Using Honeycomb-Removal and Image-Sharpening Algorithms on Visibility of 27-Gauge Endoscopic Vitrectomy." Journal of Clinical Medicine 11, no. 19 (2022): 5666. http://dx.doi.org/10.3390/jcm11195666.

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Endoscopic vitrectomy with small gauge probes has clinical potentials, but intraocular visibility is inherently limited by low resolution and dim illumination due to the reduced number of optic fibers. We investigated whether honeycomb-removal and image-sharpening algorithms, which enable real-time processing of live images with a delay of 0.004 s, can improve the visibility of 27-gauge endoscopic vitrectomy. A total of 33 images during endoscopic vitrectomy were prepared, consisting of 11 original images, 11 images after the honeycomb-removal process, and 11 images after both honeycomb-remova
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Beene, Daniel, Su Zhang, Christopher D. Lippitt, and Susan M. Bogus. "Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework." Geographies 2, no. 3 (2022): 435–52. http://dx.doi.org/10.3390/geographies2030027.

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Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end
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Nasonov, A., A. Krylov, and D. Lyukov. "IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 161–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-161-2019.

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<p><strong>Abstract.</strong> We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the edge centerlines. Compared to traditional deblurring algorithms, this approach requires only scalar blur level value rather than a blur kernel. We propose a convolutional neural network based algorithm for estimating the blur level value.</p>
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Qiu, Yi Min, Shi Hong Chen, Yi Zhou, and Xin Hai Liu. "Stereoscopic Images Enhancement Based on Edge Sharpening of Wavelet Coefficients." Applied Mechanics and Materials 511-512 (February 2014): 490–94. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.490.

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This paper proposed a new image enhancement algorithm based on edge sharpening of wavelet coefficients for stereoscopic images. Our scheme uses the multi-scale characteristic of wavelet transform, decomposes the original image into low frequency approximation sub-graph and several high frequency direction. Under the multi-scale, the low frequency approximation sub-graph is processed by edge sharpening method. Then the low frequency sub-graph decomposes in multi-scale again. At last, the low frequency approximation graph after four layers decompose sharpening and the high frequency approximatio
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Luo, Sheng Min. "A Image Enhancement Algorithm Combined Wavelet Transform with Image Fusion." Applied Mechanics and Materials 182-183 (June 2012): 1832–38. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1832.

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Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. The algorithm utilizes wavelet-based image fusion to accomplish the duplex enhancement task. Experiment results prove that the proposed enhancement algorithm can efficiently combine the merits of histogram equalization and sharpening, improving both the contrast and sharpness of the degraded image at the same time.
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Modak, Sourav, Jonathan Heil, and Anthony Stein. "Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network." Remote Sensing 16, no. 5 (2024): 874. http://dx.doi.org/10.3390/rs16050874.

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limite
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Chen, Xiaohua, Qiang Sheng, and Bhupesh Kumar Singh. "Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–9. http://dx.doi.org/10.1155/2021/5970957.

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There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the imag
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Zhu, Baoyu, Qunbo Lv, and Zheng Tan. "Adaptive Multi-Scale Fusion Blind Deblurred Generative Adversarial Network Method for Sharpening Image Data." Drones 7, no. 2 (2023): 96. http://dx.doi.org/10.3390/drones7020096.

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Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which
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Qian Weixian, 钱惟贤, 陈钱 Chen Qian, 顾国华 Gu Guohua, and 管志强 Guan Zhiqiang. "Infrared Image Sharpening Algorithm With Noise Inhibition." Acta Optica Sinica 29, no. 7 (2009): 1807–11. http://dx.doi.org/10.3788/aos20092907.1807.

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Dissertations / Theses on the topic "Image Sharpening Algorithm"

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"A novel sub-pixel edge detection algorithm: with applications to super-resolution and edge sharpening." 2013. http://library.cuhk.edu.hk/record=b5884269.

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Lee, Hiu Fung.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2013.<br>Includes bibliographical references (leaves 80-82).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstracts also in Chinese.
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Chang, Jui-Yu, and 張睿瑜. "The Study of High Efficiency HDTV Image Sharpening Algorithms." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/j98y49.

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碩士<br>長榮大學<br>經營管理研究所<br>96<br>In our daily life, digital image is always an important part for us. Moreover many standards of video coding become popular research topics, like MPEG-1, MPEG-2, H.263, MPEG-4 and H.264/AVC etc. It has great impact on video quality. Especially, MPEG-4 and H.264/AVC has been paid most attention recently. Most high definition televisions (HDTV) use MPEG or H.264 compression standard to compress the video data. When we increase the compression rate for huge amount of video stream, the block-based compression methods will generate unnatural edges between block''s bou
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Book chapters on the topic "Image Sharpening Algorithm"

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Gao, Hang, Mengting Hu, Tiegang Gao, and Renhong Cheng. "An Effective Image Detection Algorithm for USM Sharpening Based on Pixel-Pair Histogram." In Advances in Multimedia Information Processing – PCM 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00767-6_37.

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Safonov, Ilia V., Ilya V. Kurilin, Michael N. Rychagov, and Ekaterina V. Tolstaya. "Adaptive Sharpening." In Adaptive Image Processing Algorithms for Printing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6931-4_4.

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Pinheiro, Greetta, and Sonajharia Minz. "Image Quality Evaluation of Various Pan-Sharpening Techniques Using Landsat-8 Imagery." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1620-7_31.

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Teodoro, Afonso M., José M. Bioucas-Dias, and Mário A. T. Figueiredo. "Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78199-0_24.

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Liu, Feiyu, Zhewen Fang, Jiayi Zhou, Yucheng Zou, Yue Zhou, and Zhiyou Wang. "Genetic Algorithm Based Spot Location Method for Surface Plasmon Resonance Imaging Data of Microarray." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240759.

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In this research paper, we present a novel ellipse location method for surface plasmon resonance imaging (SPRi) sensing based on genetic algorithm and spatial mapping technique. By mapping the microarray information from the top view image of sensor slide to SPRi image data, we divided the microarray areas in the image data into grids. To expedite the ellipse detection process in the local grids, we employed image sharpening and a bonus-penalty mechanism to accelerate the fitness value assessment of ellipse fitting in the genetic algorithm. Different from the ellipse locating method based on OpenCV, our method can detect all the spots in both protein and small microarrays accurately. The above results indicate that our method can be applied in high-throughput SPRi detections.
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Nandibewoor, Archana, Mr Abushekh, Aman Shetty, Rahul Shirkol, Rohith S.S, and Sangamesh Biradar. "OBJECT DETECTION IN MULTISPECTRAL AERIAL IMAGES USING MACHINE LEARNING ALGORITHMS: A COMPARATIVE STUDY OF K-MEANS AND FUZZY C-MEANS APPROACHES." In Futuristic Trends in Information Technology Volume 3 Book 3. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bkit3p5ch1.

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Object detection within multispectral aerial images holds immense importance across a spectrum of applications, encompassing agriculture, environmental monitoring, surveillance, and urban planning. This research presents a comprehensive inquiry into the utilization of machine learning algorithms for detecting objects within multispectral aerial images. The approach commences by exploring a gamut of preprocessing methods, including histogram equalization, erosion, dilation, opening, closing, grayscale transformation, contrast enhancement, sharpening, and denoising. These preprocessing techniques play a pivotal role in augmenting the quality of multispectral images, thereby amplifying the efficacy of ensuing object detection algorithms. Following this, the study delves into the execution and assessment of the K-Means and Fuzzy C- Means algorithms for object detection. To gauge the performance of the proposed methodologies, a stringent evaluation involving accuracy, precision, recall, and F1-score metrics is employed. The empirical findings divulge the implications of preprocessing techniques and the subsequent algorithmic choices on the outcomes of detection. By contrasting the outcomes of the K-Means and Fuzzy C-Means methodologies, an analysis is conducted to elucidate their respective competencies and limitations within object detection contexts. This research accentuates the pivotal role of preprocessing and algorithm selection in achieving precise object detection within multispectral aerial images. By elucidating the strengths and constraints of the K-Means and Fuzzy C-Means techniques, this study lays the groundwork for future advancements in multispectral image analysis through the prism of machine learning algorithms.
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Abu Alhin Khaldoun and Niemeyer Irmgard. "A Comparative Study on Pan-SharpeningAlgorithms." In Imagin[e,g] Europe. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-494-8-1.

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There is an increased utilization of image fusion techniques for the combination of multispectral bands with higher resolution panchromatic bands of to produce so-called pan-sharpened images with both high spatial and spectral resolution. The objective of this study is to evaluate performance and quality of the following pan-sharpening algorithms: i) Discrete Wavelet Transformation (DWT), ii) &amp;Agrave; trous Wavelet Transforms fusion (ATWT), iii) Gram-Schmidt Spectral Sharpening (GS) and iv) Principal Component Spectral Sharpening (PC). We intended to evaluate the spectral and spatial quality of the pan-sharpened image by using Universal Image Quality Index (UIQI), Correlation Coefficient (CC), Variance Texture Filter and Change Class Difference. The variance texture images represent the high frequency domain data .The difference of the two variance images was used to estimate the spatial quality of the sharpened images. The variance images subtraction shows that ATWT pan-sharpening obtains the best results followed with PC and GS, whereas DWT achieves the poorest result. Applying CC as quality measure exhibits the same results; again DWT obtained the poorest correlation coefficient. The UIQI results, calculated for different land-cover classes, indicate that ATWT pan-sharpening achieves a good spectral and spatial quality compared to DWT. The percentage of changed classes indicates that both wavelet-based algorithms (ATWT and DWT) provide better quality than PC and GS pan-sharpening. Therefore the wavelet-based techniques seem to better preserve the spectral information of the original multispectral bands, whereas PC and GS come along with a larger modification of the spectral information.
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Restaino, Rocco, Gemine Vivone, Paolo Addesso, Daniele Picone, and Jocelyn Chanussot. "Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data." In Recent Advances in Image Restoration with Applications to Real World Problems. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92795.

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Multi-platform data introduce new possibilities in the context of data fusion, as they allow to exploit several remotely sensed images acquired by different combinations of sensors. This scenario is particularly interesting for the sharpening of hyperspectral (HS) images, due to the limited availability of high-resolution (HR) sensors mounted onboard of the same platform as that of the HS device. However, the differences in the acquisition geometry and the nonsimultaneity of this kind of observations introduce further difficulties whose effects have to be taken into account in the design of data fusion algorithms. In this study, we present the most widespread HS image sharpening techniques and assess their performances by testing them over real acquisitions taken by the Earth Observing-1 (EO-1) and the WorldView-3 (WV3) satellites. We also highlight the difficulties arising from the use of multi-platform data and, at the same time, the benefits achievable through this approach.
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Shaikh, Affaan. "Human Brain Imaging for Cognitive Neuroscience." In Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1281-0.ch003.

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In this chapter, a brief background of neuroimaging a human brain by data acquisition and preprocessing is provided. Neuroimaging is a medical imaging process that uses various cutting-edge technologies with artificial intelligence and machine learning to produce a clear and specific image of the brain in a non-invasive manner. Neuroimaging methods such as EEG, CT, and MRI allow researchers to directly observe brain activities from different perspectives. Data acquisition and preprocessing are essential steps in the data analysis and machine learning pipeline. They involve collecting, cleaning, and preparing raw data for further analysis or modeling. These steps are used in noise reduction, sharpening, or brightening an image, and contrast enhancement, color correction, makes it easier to identify the key features. By combining functional brain imaging with sophisticated experimental designs, data analysis methods and machine learning algorithms, functions of brain regions and their interactions can be examined and further how the neurodegenerative diseases are diagnosed.
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Conference papers on the topic "Image Sharpening Algorithm"

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Zuo, Mingcheng, Guanghui Zhao, and Dunwei Gong. "Constrained Multi-Objective Evolutionary Algorithm with Population Image Sharpening." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864795.

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Fan, Mengran, Haipeng Jia, Yunquan Zhang, Xiaojing An, and Ting Cao. "Optimizing Image Sharpening Algorithm on GPU." In 2015 44th International Conference on Parallel Processing (ICPP). IEEE, 2015. http://dx.doi.org/10.1109/icpp.2015.32.

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Ying, Liu, Ng Tek Ming, and Liew Beng Keat. "A Wavelet Based Image Sharpening Algorithm." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1631.

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Boucher, Richard H., and Virendra N. Mahajan. "Adaptive optics by image sharpening." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.fy1.

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A typical wave front sensor in an adaptive optical imaging system requires a point source or a glint on the object to operate, as do the various image-based phase retrieval methods. However, with the image sharpening method,1,2 one can sense the aberrations from the aberrated image of a point or extended object. This is done by introducing aberrations in various modes with an adaptive mirror to maximize the image sharpness. We describe simulations of image sharpening in single and multiaperture systems imaging extended objects. In simulating a multiaperture system the algorithm has been succes
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Ning Xu and Yeong-Taeg Kim. "An image sharpening algorithm for high magnification image zooming." In 2010 IEEE International Conference on Consumer Electronics (ICCE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icce.2010.5418703.

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Guan, Ruya, and Yi Wan. "An improved unsharp masking sharpening algorithm for image enhancement." In Eighth International Conference on Digital Image Processing (ICDIP 2016), edited by Charles M. Falco and Xudong Jiang. SPIE, 2016. http://dx.doi.org/10.1117/12.2243854.

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Ducay, Rey, and David W. Messinger. "Hyperspectral-multispectral image fusion using nearest-neighbor diffusion-based sharpening algorithm." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2022. http://dx.doi.org/10.1117/12.2619267.

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Li Rui and Lv Qiong. "Image sharpening algorithm based on a variety of interpolation methods." In 2012 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2012. http://dx.doi.org/10.1109/iasp.2012.6425043.

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Zhao, Li. "Design on Algorithm and Mechanism of Smog Image Sharpening Procession." In 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/icmmcce-15.2015.215.

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Li, Huixian, and Linhan Li. "A Sharpening Algorithm for Dynamic Video Image in Network Education." In 2015 International Conference on Social Science, Education Management and Sports Education. Atlantis Press, 2015. http://dx.doi.org/10.2991/ssemse-15.2015.411.

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