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Journal articles on the topic 'GRAY SCALE IMAGE'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Edan, Salam Jabbar, Maryam Nadhim Rasoul, and Amir A. Aljarrah. "A Proposed Technique for Gray Image Colorization." Webology 19, no. 1 (2022): 5099–106. http://dx.doi.org/10.14704/web/v19i1/web19343.

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The colorization of the gray-scale image is the process of using color image (has a similar "mood") to add color to a grayscale image. In this paper, we proposed new approach for colorization gray-scale images depending on Singular Value Decomposition (SVD). We used SVD to add color to a grayscale image by determining the best pixel value in the reference image, based on comparing the first column values from the left singular vector matrix of 3x3 block and the pixel value in the center of block of the gray image with other corresponding values in the reference image. Finally, use the best mat

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Eberly, David, and Jack Lancaster. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (1991): 538–49. http://dx.doi.org/10.1016/1049-9652(91)90004-4.

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Eberly, David, Jack Lancaster, and Abdalmajeid Alyassin. "On gray scale image measurements." CVGIP: Graphical Models and Image Processing 53, no. 6 (1991): 550–62. http://dx.doi.org/10.1016/1049-9652(91)90005-5.

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Kalra, G. S., and Shilpi Singh. "Efficient digital image denoising for gray scale images." Multimedia Tools and Applications 75, no. 8 (2015): 4467–84. http://dx.doi.org/10.1007/s11042-015-2484-x.

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Tian, Kai, and Jia Rong Shi. "Color Composite of Digital Radiography Image and Region Growing Defect Detection." Applied Mechanics and Materials 380-384 (August 2013): 938–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.938.

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DR image in industrial castings is usually a gray scale image and it has difficulty in simultaneously showing the defects within different thickness parts. To address this issue, the floating point type data obtained by the scanning of DR system is firstly converted into three gray scale images by the more gray stretch technique, where different gray scale images contain different thickness defects of the castings. Secondly, those three gray images are synthesized into one color image. The synthesized image contains lots of details of measured castings and its aim is to reduce the loss of imag

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Wu, Shuyue, and Jingfang Wang. "A Quantum Pointer Signal Processing Research." Indonesian Journal of Electrical Engineering and Computer Science 2, no. 3 (2016): 675. http://dx.doi.org/10.11591/ijeecs.v2.i3.pp675-683.

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<span lang="EN-US">In quantum gray-scale image processing, the storage in quantum states is the color information and the position information According to the advantage of small range of the gray scale in a gray-scale image, a novel storage expression of quantum gray-scale image is proposed and demonstrated in this study. Besides, a new concept of "quantum pointer" is put forward based on the expression. Quantum pointer is the vinculum between the information of gray-scale and position of each pixel in quantum gray-scale images. The feasibility is verified for the proposed quantum point

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Li, Ming Jing, Yu Bing Dong, and Xiao Li Wang. "Research and Development of Non Multi-Scale to Pixel-Level Image Fusion." Applied Mechanics and Materials 448-453 (October 2013): 3621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3621.

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Image fusion method based on the non multi-scale take the original image as object of study, using various fusion rule of image fusion to fuse images, but not decomposition or transform to original images. So, it can also be called simple multi sensor image fusion methods. Its advantages are low computational complexity and simple principle. Image fusion method based on the non multi-scale is currently the most widely used image fusion methods. The basic principle of fuse method is directly to select large gray, small gray and weighted average among pixel on the source image, to fuse into a ne

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Reddy, V. Varshith, Y. Shiva Krishna, U. Varun Kumar Reddy, and Shubhangi Mahule. "Gray Scale Image Captioning Using CNN and LSTM." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 1566–71. http://dx.doi.org/10.22214/ijraset.2022.41589.

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Abstract: The objective of the project is to generate caption of an image. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. With the advancement in Deep learning techniques and availability of huge datasets and computer power, we can build models that can generate captions for an image. This is what we have implemented in this Python based project where we have used the deep learning techniques of CNN (Convolutional Neural Networks) and LSTM (Long

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CHENG, JIN-CHANG, and HON-SON DON. "SEGMENTATION OF BILEVEL IMAGES USING MATHEMATICAL MORPHOLOGY." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 04 (1992): 595–628. http://dx.doi.org/10.1142/s0218001492000321.

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This paper presents the results of a study on the use of morphological skeleton transformation to segment gray-scale images into bilevel images. When a bilevel image (such as printed texts and machine tools) is digitized, the result is a gray-scale image due to the point spread function of digitizer, non-uniform illumination and noise. Our method can recover the original bilevel image from the gray-scale image. The theoretical basis of the algorithm is the physical structure of the skeleton set. A connectivity property of the gray-scale skeleton transformation is used to separate and remove th

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Gürdal, P., C. F. Hildebolt, and B. G. Akdeniz. "The effects of different image file formats and image-analysis software programs on dental radiometric digital evaluations." Dentomaxillofacial Radiology 30, no. 1 (2001): 50–55. http://dx.doi.org/10.1038/sj/dmfr/4600570.

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OBJECTIVES To determine if variations in radiodensity data are introduced by lossy Joint Photographic Experts Group (JPEG) compression and/or the use of three software programs. METHODS An occlusal film with an aluminium step wedge was exposed, processed and digitized under standard conditions. Before the image was saved, the coordinates and the gray-scale value for each pixel in a 20 x 20 pixel area near the middle of the thickest step were recorded. These pixel coordinates and gray-scale values served as Truth 1. In addition, a digital simulated-radiographic image with assigned gray-scale va

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Yang, Yu-Guang, Qian-Qian Zhao, and Si-Jia Sun. "Novel quantum gray-scale image matching." Optik 126, no. 22 (2015): 3340–43. http://dx.doi.org/10.1016/j.ijleo.2015.08.010.

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12

G, Srinivasa Rao, Sri Krishna A, Mahaboob Basha S, and Prakash Ch. Jeevan. "Object-Based Image Enhancement Technique for Gray Scale Images." International Journal of Advanced Information Technology 4, no. 3 (2014): 9–23. http://dx.doi.org/10.5121/ijait.2014.4302.

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Hayder, Israa M., Hussain A. Younis, and Hameed Abdul-Kareem Younis. "Digital Image Enhancement Gray Scale Images In Frequency Domain." Journal of Physics: Conference Series 1279 (July 2019): 012072. http://dx.doi.org/10.1088/1742-6596/1279/1/012072.

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Zhang, Shu Wen, Xiao Ning Zhang, Zhi Yong Wu, and Li Wan Shi. "Research on Asphalt Mixture Injury Digital Image Based on Enhancement and Segmentation Processing Technology." Applied Mechanics and Materials 470 (December 2013): 832–37. http://dx.doi.org/10.4028/www.scientific.net/amm.470.832.

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In order to visually observe the damage evolution of the asphalt mixture, and analyze the damage evolution accurately and quantitatively, the CT image of asphalt mixture image was processed for image enhancing and segmenting, the entire or partial image features can be effectively improved. The dynamics gray-scale range of the image was adjusted by the histogram equalization and provision. Image gray-scale transformation is enhanced by using Matlab software. At the same time, when the algebraic operations and noise filtering is used to process, the mixture image segmentation and boundary ident

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QIAN, KAI, SIQI CAO, and PRABIR BHATTACHARYA. "GRAY IMAGE SKELETONIZATION WITH HOLLOW PREPROCESSING USING DISTANCE TRANSFORMATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (1999): 881–92. http://dx.doi.org/10.1142/s0218001499000483.

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This paper presents a gray-scale skeletonization algorithm based on the gray weighted distance transformation (GWDT). The proposed algorithm is conceptually simple and could be used to process nonuniformly distributed gray-scale images. The algorithm can detect the hollows on the gray-scale image, a feature that is often ignored in other skeletonization algorithms, and improves the quality of the skeleton.

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Qomariyah, Nurul, Rahadi Wirawan, Ni Kadek Nova Anggarani, Laili Mardiana, and Kasnawi Alhadi. "Karakteristik Gaharu Grynops Vertegii (Gilg.) Domke Berdasarkan Analisis Sebaran Gray scale Level." EIGEN MATHEMATICS JOURNAL 1, no. 1 (2019): 44. http://dx.doi.org/10.29303/emj.v1i1.27.

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Agarwood Grynops Vertegii (Gilg.) Domke is a type of agarwood that is widely cultivated in the NTB area. The economic value of Agarwood is directly proportional to its quality. Color is one of the physical parameters to determine the quality of agarwood. The purpose of this study is to classify Grynops Vertegii (Gilg.) Agarwood based on the distribution of gray scale level using image processing. The method used is image processing based on gray scale level, Agarwood is divided into four classes based on the dominant color, in this study all samples divided into four classes: A, B, C, and D. I

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Xue, Guang Hui, Bao Hua Hu, Xin Ying Zhao, Er Meng Liu, and Wei Jian Ding. "Study on Characteristic Extraction of Coal and Rock at Mechanized Top Coal Caving Face Based on Image Gray Scale." Applied Mechanics and Materials 678 (October 2014): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.678.193.

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A method on the feature extraction of coal and rock character recognition was mainly put forward based on image gray level distribution and gray scale average value. In order to improve the recognition effects of the image, cropping, gray level transformation, contrast enhancement, median filtering and other preprocessing work were applied individually on the raw image of coal caving and rock caving acquired from mechanized top caving face, then gray histogram of image signal of coal and rock was abstracted and the gray scale mean were calculated. The results shows that (1) the range of gray s

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Ye, Xiufen, Haibo Yang, Chuanlong Li, Yunpeng Jia, and Peng Li. "A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex." Remote Sensing 11, no. 11 (2019): 1281. http://dx.doi.org/10.3390/rs11111281.

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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale corr

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LIN, MIN-HUI, YU-CHEN HU, and CHIN-CHEN CHANG. "BOTH COLOR AND GRAY SCALE SECRET IMAGES HIDING IN A COLOR IMAGE." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 06 (2002): 697–713. http://dx.doi.org/10.1142/s0218001402001903.

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In the past, most image hiding techniques have been applied only to gray scale images. Now, many valuable images are color images. Thus, it has become important to be able to apply image-hiding techniques to hide color images. In this paper, our proposed scheme can not only be applied to "a color host image hiding a color secret image", but also to "a color host image hiding a gray scale secret image". Our scheme utilizes the rightmost 3, 2 and 3 bits of the R, G, B channels of every pixel in the host image to hide related information from the secret image. Meanwhile, we utilize the leftmost 5

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Siqueira, Gustavo Lopes Gomes de, Robson Pequeno de Sousa, Ricardo Alves de Olinda, Carlos Alberto Engelhorn, André Luiz Siqueira da Silva, and Juliana Gonçalves Almeida. "Proposal for computer-aided diagnosis based on ultrasound images of the kidney: is it possible to compare shades of gray among such images?" Radiologia Brasileira 54, no. 1 (2021): 27–32. http://dx.doi.org/10.1590/0100-3984.2019.0138.

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Abstract Objective: To compare ultrasound images of the kidney obtained, randomly or in a controlled manner (standardizing the physical aspects of the ultrasound system), by various professionals and with different devices. Materials and Methods: We evaluated a total of 919 images of kidneys, obtained by five professionals using two types of ultrasound systems, in 24 patients. The images were categorized into four types, by how they were acquired and processed. We compared the gray-scale median and different gray-scale ranges representative of virtual histological tissues. Results: There were

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Kondo, Takashi, and Xiaohua Zhang. "Coloring of Gray Scale Image Using Image Color Transfer." Journal of The Institute of Image Information and Television Engineers 61, no. 6 (2007): 838–41. http://dx.doi.org/10.3169/itej.61.838.

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Chopra, Deepshikha. "Lsb Based Digital Image Watermarking For Gray Scale Image." IOSR Journal of Computer Engineering 6, no. 1 (2012): 36–41. http://dx.doi.org/10.9790/0661-0613641.

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23

Lakshmi, Muthu, Ganesh Kumar, Bala Subramanian, and Priti Rishi. "MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES." Latin American Applied Research - An international journal 52, no. 2 (2022): 167–72. http://dx.doi.org/10.52292/j.laar.2022.583.

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Abstract-- The main objective of this paper is to examine the performance of Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image.

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Mrs.S.Gandhimathi, @. Usha, Rani M.Jeya, S.Sneha, and R.Kalaiselvi. "FEATURE EXTRACTION BASED RETRIEVAL OF GEOGRAPHIC IMAGES." International Journal of Computational Science and Information Technology (IJCSITY) 2, May (2019): 1–9. https://doi.org/10.5281/zenodo.3532468.

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<strong>ABSTRACT </strong> This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the

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Wang, Lu, Bin Yan, Hong-Mei Yang, and Jeng-Shyang Pan. "Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images." Symmetry 13, no. 1 (2020): 65. http://dx.doi.org/10.3390/sym13010065.

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Visual cryptography (VC) has found numerous applications in privacy protection, online transaction security, and voting security, etc. To counteract potential cheating attacks, Lin et al. proposed flip visual cryptography in 2010, where a second secret image can be revealed by stacking one share with a flipped version of another share. The second secret image can be designed as an additional verification mechanism. However, Lin’s scheme produces meaningless shares and is only applicable to binary secret images. It is interesting to explore whether it is possible to extend the flip VC to having

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Kulkarni, Asawari, and Aparna Junnarkar. "Gray-Scale Image Compression Techniques: A Review." International Journal of Computer Applications 131, no. 13 (2015): 22–25. http://dx.doi.org/10.5120/ijca2015907519.

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Sharkawy, Nada, Yasmine Afify, Walaa Gad, and Nagwa Badr. "SILENT MUTATION FOR GRAY-SCALE IMAGE ENCRYPTION." International Journal of Intelligent Computing and Information Sciences 23, no. 4 (2023): 112–27. http://dx.doi.org/10.21608/ijicis.2023.227726.1287.

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Hassan, Ammar A. "COLORING OF GRAY-SCALE IMAGE USING FPGA." Journal of Engineering 16, no. 04 (2024): 5932–45. http://dx.doi.org/10.31026/j.eng.2010.04.17.

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Sim, K. S., C. P. Tso, and Y. Y. Tan. "Recursive sub-image histogram equalization applied to gray scale images." Pattern Recognition Letters 28, no. 10 (2007): 1209–21. http://dx.doi.org/10.1016/j.patrec.2007.02.003.

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Fan, Zehua, Desheng Wang, and Nannan Zhang. "Monitoring of Nitrogen Transport Data in Pear Leaves Based on Infrared Spectroscopy." Journal of Chemistry 2022 (June 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/1547582.

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In order to better monitor the data of nitrogen transport in pear leaves, a method based on infrared spectroscopy was proposed. The near-infrared reflection spectrum imaging technology is used to collect the leaf scale spectral image of the target crop. Computer image analysis software is used to process the spectral digital image and extract the spectral data. After statistical analysis, the data are selected as variables. Combined with the chemical analysis test results, the crop nutrition detection model is established, and the conclusion is drawn. The experimental results show that the ban

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Nidhal, K. EL Abbadi, and Saleem Eman. "Automatic gray images colorization based on lab color space." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1501–9. https://doi.org/10.11591/ijeecs.v18.i3.pp1501-1509.

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The colorization aims to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. The gray image concatenates wit

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Huang, Zhi Kai, Xing Wang Zhang, Wei Zhong Zhang, and Ling Ying Hou. "A New Embossing Method for Gray Images Using Kalman Filter." Applied Mechanics and Materials 39 (November 2010): 488–91. http://dx.doi.org/10.4028/www.scientific.net/amm.39.488.

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In this paper, we propose a new embossing algorithm for gray images using Kalman filter. First, a 2D gray image is first converted to a one dimension vector; those vectors could be considered as a one-dimension discrete-time signal. Then, the performance of image filtering using Kalman filter for image is studied and according to its results, Canny edge detection operators are investigated to find edge map in a gray scale image. Finally, enhance contrast using histogram equalization has been applied. Compared with other conventional embossing method for images, it is an impressive experimental

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Kim, Sang-Yup, and Seong-Whan Lee. "Gray-Scale Nonlinear Shape Normalization Method for Handwritten Oriental Character Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (1998): 81–95. http://dx.doi.org/10.1142/s0218001498000075.

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In general, nonlinear shape normalization methods for binary images have been used in order to compensate for the shape distortions of handwritten characters. However, in most document image analysis and recognition systems, a gray-scale image is first captured and digitized using a scanner or a video camera, then a binary image is extracted from the original gray-scale image using a certain extraction technique. This binarization process may remove some useful information of character images such as topological features, and introduce noises to character background. These errors are accumulat

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He, Xiao Qin, Jin Jun Li, and Xiao Yan Li. "Multi-Scale Stereo Analysis Based on Local Multi-Model Monogenic Image Feature Descriptors." Advanced Materials Research 433-440 (January 2012): 853–59. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.853.

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A multi-scale method based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal and local mean colors in the multi-scale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orientation a

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Li, Jinjun, Hong Zhao, Chengying Shi, and Xiang Zhou. "A Multi-Model Stereo Similarity Function Based on Monogenic Signal Analysis in Poisson Scale Space." Mathematical Problems in Engineering 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/202653.

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A stereo similarity function based on local multi-model monogenic image feature descriptors (LMFD) is proposed to match interest points and estimate disparity map for stereo images. Local multi-model monogenic image features include local orientation and instantaneous phase of the gray monogenic signal, local color phase of the color monogenic signal, and local mean colors in the multiscale color monogenic signal framework. The gray monogenic signal, which is the extension of analytic signal to gray level image using Dirac operator and Laplace equation, consists of local amplitude, local orien

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Bawaneh, Mohammed J., and Atef A. Obeidat. "A Secure Robust Gray Scale Image Steganography Using Image Segmentation." Journal of Information Security 07, no. 03 (2016): 152–64. http://dx.doi.org/10.4236/jis.2016.73011.

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Abbadi, Nidhal K. El, and Eman Saleem Razaq. "Automatic gray images colorization based on lab color space." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1501. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1501-1509.

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<p>The colorization aim to transform a black and white image to a color image. This is a very hard issue and usually requiring manual intervention by the user to produce high-quality images free of artifact. The public problem of inserting gradients color to a gray image has no accurate method. The proposed method is fully automatic method. We suggested to use reference color image to help transfer colors from reference image to gray image. The reference image converted to Lab color space, while the gray scale image normalized according to the lightness channel L. the gray image concaten

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Jiang, Bo, Wanxu Zhang, Jian Zhao, et al. "Gray-Scale Image Dehazing Guided by Scene Depth Information." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7809214.

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Combined with two different types of image dehazing strategies based on image enhancement and atmospheric physical model, respectively, a novel method for gray-scale image dehazing is proposed in this paper. For image-enhancement-based strategy, the characteristics of its simplicity, effectiveness, and no color distortion are preserved, and the common guided image filter is modified to match the application of image enhancement. Through wavelet decomposition, the high frequency boundary of original image is preserved in advance. Moreover, the process of image dehazing can be guided by the imag

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Dawlat, Mustafa Sulaiman, Mohsin Abdulazeez Adnan, and Haron Habibollah. "Double stages of feature extarction-based GFPMI for colored finger vein identification." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 2 (2020): 927–37. https://doi.org/10.11591/ijeecs.v18.i2.pp927-937.

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Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored local

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Fedorov, E. E., O. L. Khramova-Baranova, T. Y. Utkina, Ya M. Kozhushko, and I. O. Nesen. "THE INTELLECTUAL ANALYSIS METHOD OF COLOR IMAGES." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 45–55. https://doi.org/10.15588/1607-3274-2025-2-4.

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Context. Automatic and automated image analysis methods used in computer graphic design, biometric identification, and military target search are now widespread. The object of the research is the process of color image analysis.Objective. The goal of the work is to create an intelligent method of image analysis based on quantization, binarization and clustering.Method. The proposed method for intelligent color image analysis consists of the following techniques. The technique of reducing the number of colors based on the conversion of a color image into a gray-scale image and quantization of t

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Wang, Jin, Yifei Cui, Hao Wang, Mohammad Ikbal, and Mohammad Usama. "Analysis of Extraction Algorithm for Visual Navigation of Farm Robots Based on Dark Primary Colors." International Journal of Agricultural and Environmental Information Systems 12, no. 2 (2021): 61–72. http://dx.doi.org/10.4018/ijaeis.20210401.oa5.

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In order to quickly extract the visual navigation line of farmland robot, an extraction algorithm for dark primary agricultural machinery is proposed. The application of dark primary color principle in new farmland is made clearer by gray scale method, and the soil and crops are obviously separated, and the image processing technology of visual navigation line image of farmland is realized. In binary filtering of gray scale images, the maximum interclass variance method and morphological method are used respectively. The researchers use vertical projection method and least square method to the

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Soni, Ritu, and Chouhan Siddharth Singh. "Super-Resolution and De-convolution for Single/Multi Gray Scale Images Using SIFT Algorithm." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 07 (2014): 1024–29. https://doi.org/10.5281/zenodo.14743009.

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This paper represent a Blind algorithm that restore the blurred images for single image and multi-image blur de-convolution and multi-image super-resolution on low-resolution images deteriorated by additive white Gaussian noise ,the aliasing and linear space-invariant. Image De-blurring is a field of Image Processing in which recovering an original and sharp image from a corrupted image. Proposed method is based on alternating minimization algorithm with respect to unidentified blurs and high-resolution image and the Huber-markov random field(HMRF) model for its ability to preserve discontinui

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Shen, Kuan, Yumei Wen, and Yufang Cai. "Efficient x-ray image enhancement algorithm using image fusion." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 17, no. 3 (2009): 207–20. http://dx.doi.org/10.3233/xst-2009-022300223.

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Multiresolution Analysis (MRA) plays an important role in image and signal processing fields, and it can extract information at different scales. Image fusion is a process of combining two or more images into an image, which extracts features from source images and provides more information than one image. The research presented in this article is aimed at the development of an automated imaging enhancement system in digital radiography (DR) images, which can clearly display all the defects in one image and don't bring blocking effect. In terms of characteristic of the collected radiographic s

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Sun, Jiawei. "Colorization of gray scale images based on convolutional block attention and Pix2Pix network." Journal of Physics: Conference Series 2646, no. 1 (2023): 012032. http://dx.doi.org/10.1088/1742-6596/2646/1/012032.

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Abstract Image colorization is a key research direction in the field of computer vision, which aims to predict the color information of grayscale images and convert them into color images. Traditional image colorization methods need to provide a large number of color points to mark each object in the image. As a result of the rapid development of convolutional neural networks, image colorization methods have also improved, with deep learning methods benefiting the most. However, due to the lack of feature quality, there are still many challenges in practical applications, such as boundary blur

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Zeng Fanfeng, and Shang Wensong. "Image Gray-Scale Enhancement Algorithm Based on Interception." Journal of Convergence Information Technology 7, no. 23 (2012): 501–7. http://dx.doi.org/10.4156/jcit.vol7.issue23.59.

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R, Reka. "DIRECTIONAL THRESHOLDING ALGORITHM FOR GRAY SCALE IMAGE SEGMENTATION." International Journal of Advances in Signal and Image Sciences 4, no. 1 (2018): 23. http://dx.doi.org/10.29284/ijasis.4.1.2018.23-29.

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Rosyadi, Imron. "Gray-Scale Image Colorization Using Various Affinity Functions." Dinamika Rekayasa 8, no. 1 (2012): 7. http://dx.doi.org/10.20884/1.dr.2012.8.1.52.

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<p>In this paper, we have proposed, implemented, and compared some affinity functions for an image colorization algorithm. The colorization qualityof the proposed affinityfunctions was just slightly behind the original functions, while one of the proposed functions performed faster than the original affinity function. We also implemented the colorization algorithm for coloring an Indonesian historical image.</p>

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Anastassiou, D., W. Pennebaker, and J. Mitchell. "Gray-Scale Image Coding for Freeze-Frame Videoconferencing." IEEE Transactions on Communications 34, no. 4 (1986): 382–94. http://dx.doi.org/10.1109/tcom.1986.1096538.

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Yuan, Liying, Junfeng Wu, and Shuangquan Li. "Improved Wavelet Threshold for Gray Scale Image Denoising." International Journal of Signal Processing, Image Processing and Pattern Recognition 7, no. 3 (2014): 45–52. http://dx.doi.org/10.14257/ijsip.2014.7.3.05.

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Naseri, Mosayeb, Mona Abdolmaleky, Fariborz Parandin, Negin Fatahi, Ahmed Farouk, and Reza Nazari. "A New Quantum Gray-Scale Image Encoding Scheme." Communications in Theoretical Physics 69, no. 2 (2018): 215. http://dx.doi.org/10.1088/0253-6102/69/2/215.

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