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Статті в журналах з теми "Histogram correction"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-311-2016.

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Анотація:

Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.

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2

Gao, Hui-ting, Wei Liu, Hong-yan He, Bing-xian Zhang, and Cheng Jiang. "DE-STRIPING FOR TDICCD REMOTE SENSING IMAGE BASED ON STATISTICAL FEATURES OF HISTOGRAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 311–16. http://dx.doi.org/10.5194/isprs-archives-xli-b1-311-2016.

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Анотація:

Aim to striping noise brought by non-uniform response of remote sensing TDI CCD, a novel de-striping method based on statistical features of image histogram is put forward. By analysing the distribution of histograms,the centroid of histogram is selected to be an eigenvalue representing uniformity of ground objects,histogrammic centroid of whole image and each pixels are calculated first,the differences between them are regard as rough correction coefficients, then in order to avoid the sensitivity caused by single parameter and considering the strong continuity and pertinence of ground objects between two adjacent pixels,correlation coefficient of the histograms is introduces to reflect the similarities between them,fine correction coefficient is obtained by searching around the rough correction coefficient,additionally,in view of the influence of bright cloud on histogram,an automatic cloud detection based on multi-feature including grey level,texture,fractal dimension and edge is used to pre-process image.Two 0-level panchromatic images of SJ-9A satellite with obvious strip noise are processed by proposed method to evaluate the performance, results show that the visual quality of images are improved because the strip noise is entirely removed,we quantitatively analyse the result by calculating the non-uniformity ,which has reached about 1% and is better than histogram matching method.

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3

Hildebolt, C. F., R. K. Walkup, G. L. Conover, N. Yokoyama-Crothers, T. Q. Bartlett, M. W. Vannier, M. K. Shrout, and J. J. Camp. "Histogram-matching and histogram-flattening contrast correction methods: a comparison." Dentomaxillofacial Radiology 25, no. 1 (January 1996): 42–47. http://dx.doi.org/10.1259/dmfr.25.1.9084285.

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4

Zhang, Jing, and Guang Xue Chen. "Research on the Color Correction Algorithm of Images Based on Histogram Matching." Applied Mechanics and Materials 469 (November 2013): 256–59. http://dx.doi.org/10.4028/www.scientific.net/amm.469.256.

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Анотація:

Different rendering conditions (e.g., changes in lighting conditions or atmospheric conditions, changes of the imaging system) often cause significant color differences between two images. In the prepress process, the brightness and hue between two images should be adjusted to be as similar as possible. Currently, we generally use image processing software such as PhotoShop to adjust the image manually, it’s complex and time consuming. In this paper, the color correction algorithm based on histogram matching was put forward and implemented. Only one image needed to be adjusted well previously as the reference image, and the mapping relationship was established on pixels between the histogram of the source images and the reference image, then the source images would have the histograms similar to that of the reference image, so that the images would have similar color characteristic and achieve image color correction finally. The experimental result showed that the realized color correction algorithm was effective, it could not only maintain the visual effect of images, but also eliminate the color differences between the reference image and the source images.

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5

Stolk, Ad, Torbjörn E. Törnqvist, Kilian P. V. Hekhuis, Henk J. A. Berendsen, and Johannes van der Plicht. "Calibration of 14C Histograms: A Comparison of Methods." Radiocarbon 36, no. 1 (1994): 1–10. http://dx.doi.org/10.1017/s0033822200014272.

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The interpretation of 14C histograms is complicated by the non-linearity of the 14C time scale in terms of calendar years, which may result in clustering of 14C ages in certain time intervals unrelated to the (geologic or archaeologic) phenomenon of interest. One can calibrate 14C histograms for such distortions using two basic approaches. The KORHIS method constructs a 14C histogram before calibration is performed by means of a correction factor. We present the CALHIS method based on the Groningen calibration program for individual 14C ages. CALHIS first calibrates single 14C ages and then sums the resulting calibration distributions, thus yielding a calibrated 14C histogram. The individual calibration distributions are normalized to a standard Gaussian distribution before superposition, thus allowing direct comparison among various 14C histograms. Several experiments with test data sets demonstrate that CALHIS produces significantly better results than KORHIS. Although some problems remain (part of the distortions due to 14C variations cannot be eliminated), we show that CALHIS offers good prospects for using 14C histograms, particularly with highly precise and accurate 14C ages.

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6

Wand, M. P. "Correction: Data-Based Choice of Histogram Bin Width." American Statistician 53, no. 2 (May 1999): 174. http://dx.doi.org/10.2307/2685743.

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7

Kholmovski, Eugene G., Andrew L. Alexander, and Dennis L. Parker. "Correction of slab boundary artifact using histogram matching." Journal of Magnetic Resonance Imaging 15, no. 5 (April 26, 2002): 610–17. http://dx.doi.org/10.1002/jmri.10094.

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8

Yi, Zeguang, Nan Pan, Yi Liu, and Yu Guo. "Study of laser displacement measurement data abnormal correction algorithm." Engineering Computations 34, no. 1 (March 6, 2017): 123–33. http://dx.doi.org/10.1108/ec-10-2015-0325.

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Purpose This paper aims to reduce and eliminate the abnormal peaks which, because of the reflection in the process of laser detection, make it easier to proceed with further analysis. Design/methodology/approach To solve the above problem, an abnormal data correction algorithm based on histogram, K-Means clustering and improved robust locally weighted scatter plot smoothing (LOWESS) is put forward. The proposed algorithm does section leveling for shear plant first and then applies histogram to define the abnormal fluctuation data between the neighboring points and utilizes a K-Means clustering to eliminate the abnormal data. After that, the improved robust LOWESS method, which is based on Euclidean distance, is used to remove the noise interference and finally obtain the waveform characteristics for next data processing. Findings The experiment result of liner tool mark laser test data correction demonstrates the accuracy and reliability of the proposed algorithm. Originality/value The study enables the following points: the detection signal automatic leveling; abnormal data identification and demarcation using K-Means clustering and histogram; and data smoothing using LOWESS.

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9

Kim, Seaho, and Hiseok Kim. "Luminance Correction for Stereo Images using Histogram Interval Calibration." Journal of the Institute of Electronics Engineers of Korea 50, no. 12 (December 25, 2013): 159–67. http://dx.doi.org/10.5573/ieek.2013.50.12.159.

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10

Shuang, Zhang, Jin Gang, and Qin Yu-ping. "Gray Imaging Extended Target Tracking Histogram Matching Correction Method." Procedia Engineering 15 (2011): 2255–59. http://dx.doi.org/10.1016/j.proeng.2011.08.422.

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11

Eidous, Omar M. "Bias correction for histogram estimator using line transect sampling." Environmetrics 16, no. 1 (2005): 61–69. http://dx.doi.org/10.1002/env.671.

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12

Khan, M. Ryyan, Hafiz Imtiaz, and Md Kamrul Hasan. "Show-through correction in scanned images using joint histogram." Signal, Image and Video Processing 4, no. 3 (June 24, 2009): 337–51. http://dx.doi.org/10.1007/s11760-009-0124-5.

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13

Qiu, Richard L. J., Yang Lei, Joseph Shelton, Kristin Higgins, Jeffrey D. Bradley, Walter J. Curran, Tian Liu, Aparna H. Kesarwala, and Xiaofeng Yang. "Deep learning-based thoracic CBCT correction with histogram matching." Biomedical Physics & Engineering Express 7, no. 6 (October 29, 2021): 065040. http://dx.doi.org/10.1088/2057-1976/ac3055.

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Abstract Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.

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14

Chinegeram, Kalyani, Ramudu Kama, and Ganta Raghotham Reddy. "Enhancement and Segmentation of Medical Images Using AGCWD and ORACM." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 13 (November 17, 2020): 45. http://dx.doi.org/10.3991/ijoe.v16i13.18501.

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<p>Images that are obtained in the real world in low contrast are inappropriate for human eyes to read the medical images. Enhancement and segmentation have an important role to play in digital image processing, pattern recognition, and the computer vision. Here, this paper presents an effective way of changing histograms and improving contrast in digital images. Segmentation is done on AGCWD enhanced images. Histogram equalization is an important technique for contrast enhancement. Nevertheless, modern Histogram Equalization commonly results in unnecessary contrast enhancement, which in turn offers an un-natural presence to the processed image and produces visual artifacts. We present an automated transformation technique that helps boost dimmed image brightness by gamma correction and weighted distribution, commonly known as Adaptive Gamma Correction Weighted Distribution (AGCWD). The contrast enhancement level can be modified using this technique; noise robustness, white or black stretching, and the protection of medium brightness can be easily integrated into the optimization process. Finally, a contrast enhancement algorithm with low complexity is introduced. All the process of enhancement will be done during the process of pre-processing the image. Later, in post-processing, we introduce a specific level set method known as ORACM for better segmentation of an enhanced AGCWD image, and it is compared with the traditional level set method.</p>

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15

Ao, Jun, and Chunbo Ma. "Adaptive Stretching Method for Underwater Image Color Correction." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (November 12, 2017): 1854001. http://dx.doi.org/10.1142/s0218001418540010.

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The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computational cost low at the same time. To achieve adaptability, the adaptable threshold is deduced from the histogram of image. Performance analysis reveals that the proposed method significantly enhances the image contrast, reduces the color cast and meanwhile, keeps the computational consumption low.

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16

Bhaskara Rao, Jana, K. V G Srinivas, A. Siva kumar, and J. Beatrice Seventline. "Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation." ECS Transactions 107, no. 1 (April 24, 2022): 1441–57. http://dx.doi.org/10.1149/10701.1441ecst.

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In image processing, enhancement histogram equalization is the widely used technique for contrast enhancement. However, this technique tends to change the brightness of the image. Here, the contrast and resolution of image were enhanced using the proposed Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation (BHBI) technique. Bi Histogram for contrast enhancement and bicubic interpolation for resolution enhancement has taken. Bi Histogram Equalization separates the input image's histogram into two based on input mean before equalizing them independently. Bicubic Interpolation can generate bigger or high-resolution image from one or more low resolution or smaller images. The performance of the BHBI method can be compared for some typical image’s cameraman lens that are applied to some existing enhancement methods like Adaptive Gamma Correction with Weighting Distribution (AGCWD), Adaptive Scale Adjustment Design of Unsharp Masking Filters (ASAUMF), Averaging Histogram Equalization (AVGHEQ), and Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The performance of these existing techniques can be evaluated subjectively in terms of person illustration observation and measurably using Discrete Entropy (DE), Image Quality Index (IQI), Normalized Correlation Coefficient (NCC), Contrast Improvement Index (CII), and Absolute Mean Brightness Error (AMBE). The results obtained from the BHBI technique shows better when compared with respect to the various existing techniques.

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17

WANG, Yong, Fen-Xiong CHEN, and Hong-Xiang GUO. "Kernel Spatial Histogram Target Tracking Based on Template Drift Correction." Acta Automatica Sinica 38, no. 3 (December 21, 2012): 430–36. http://dx.doi.org/10.3724/sp.j.1004.2012.00430.

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18

Chang, Yakun, Cheolkon Jung, Peng Ke, Hyoseob Song, and Jungmee Hwang. "Automatic Contrast-Limited Adaptive Histogram Equalization With Dual Gamma Correction." IEEE Access 6 (2018): 11782–92. http://dx.doi.org/10.1109/access.2018.2797872.

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19

Wan, Minjie, Guohua Gu, Weixian Qian, Kan Ren, Qian Chen, and Xavier Maldague. "Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction." Remote Sensing 10, no. 5 (April 27, 2018): 682. http://dx.doi.org/10.3390/rs10050682.

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20

Wang, Xin Yang, Huan Xia Feng, and Jian Zhang. "A Correction Algorithm of Multi-View Image System." Applied Mechanics and Materials 631-632 (September 2014): 395–98. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.395.

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This paper, based on the Retinex Color Constancy Theory, proposes a novel multi-view image correction algorithm, which correction effect is very effective. Histogram equalization,retinex processing and color restoration are performed for multi-view images,and extract reflectance which describe object intrinsic properties to eliminate un-consistent light source influence.The experimental results show that corrected images not only have high color contract,also have consistent color appearance with others.

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21

Zhuang, Liyun, and Yepeng Guan. "Image Enhancement Using Modified Histogram and Log-Exp Transformation." Symmetry 11, no. 8 (August 20, 2019): 1062. http://dx.doi.org/10.3390/sym11081062.

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An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.

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22

Zhou, Yan, He Jiang, Zhe Wang, Xiaoxia Yang, and Erhui Geng. "ASSESSMENT OF FOUR TYPICAL TOPOGRAPHIC CORRECTIONS IN LANDSAT TM DATA FOR SNOW COVER AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 157–62. http://dx.doi.org/10.5194/isprsarchives-xli-b1-157-2016.

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The accuracy of snow cover information extraction in remote-sensing images dependent on a variety of factors, especially in mountain area with complex terrain. This paper aims at analyzing the accuracy of snow cover information extraction from remot esensing images, using Landsat5 TM images and DEM data, with the study area of Xinjiang Tianshan, measuring topographic correction effects of Cosine correction, C correction, SCS correction, and SCS + C correction from four aspects: visual comparison, standard deviation, correlation analysis and histogram, then extract snow cover area for study area. Results showed that C correction and SCS+C correction performed better among four classic terrain correction models, which indicated changes in snow pixel rat io after correction with variation range of 2% , and correlation coefficient of each band is highest before and after correction.

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23

Zhou, Yan, He Jiang, Zhe Wang, Xiaoxia Yang, and Erhui Geng. "ASSESSMENT OF FOUR TYPICAL TOPOGRAPHIC CORRECTIONS IN LANDSAT TM DATA FOR SNOW COVER AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 157–62. http://dx.doi.org/10.5194/isprs-archives-xli-b1-157-2016.

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Анотація:

The accuracy of snow cover information extraction in remote-sensing images dependent on a variety of factors, especially in mountain area with complex terrain. This paper aims at analyzing the accuracy of snow cover information extraction from remot esensing images, using Landsat5 TM images and DEM data, with the study area of Xinjiang Tianshan, measuring topographic correction effects of Cosine correction, C correction, SCS correction, and SCS + C correction from four aspects: visual comparison, standard deviation, correlation analysis and histogram, then extract snow cover area for study area. Results showed that C correction and SCS+C correction performed better among four classic terrain correction models, which indicated changes in snow pixel rat io after correction with variation range of 2% , and correlation coefficient of each band is highest before and after correction.

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24

Miura, Kota. "Bleach correction ImageJ plugin for compensating the photobleaching of time-lapse sequences." F1000Research 9 (December 21, 2020): 1494. http://dx.doi.org/10.12688/f1000research.27171.1.

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During the capturing of the time-lapse sequence of fluorescently labeled samples, fluorescence intensity exhibits decays. This phenomenon is known as 'photobleaching' and is a widely known problem in imaging in life sciences. The photobleaching can be attenuated by tuning the imaging set-up, but when such adjustments only partially work, the image sequence can be corrected for the loss of intensity in order to precisely segment the target structure or to quantify true intensity dynamics. We implemented an ImageJ plugin that allows the user to compensate for the photobleaching to estimate the non-bleaching condition with choice of three different algorithms: simple ratio, exponential fitting, and histogram matching methods. The histogram matching method is a novel algorithm for photobleaching correction. This article presents details and characteristics of each algorithm based on application to actual image sequences.

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25

Hang, Yuan. "Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/9917538.

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In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. In this paper, we illustrate a method which involves the combination of the deep features with the conventional features together to form a hybrid feature space. Several image enhancement techniques, such as histogram equalization, Laplacian operator, logarithm transform, and Gamma correction, are undertaken to improve the quality and characteristics of the image before feature extraction. Among these methods, applying histogram equalization not only improves the brightness and contrast of the image but also achieves the highest classification accuracy at 69.8%. We extract features such as histograms of oriented gradients, local binary pattern, SIFT, and SURF and combine them with deep features of residual generative adversarial network. We compare the ResNet18, a residual convolutional neural network with 18 layers, with the Res-GAN, a residual generative adversarial network. The experimental result shows that Res-GAN outperforms the former model. Besides, we fuse SURF with deep features with a random forest model as a classifier, which achieves 95% accuracy.

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26

Ashiba, M. I., M. S. Tolba, A. S. El-Fishawy, and F. E. Abd El-Samie. "Gamma correction enhancement of infrared night vision images using histogram processing." Multimedia Tools and Applications 78, no. 19 (June 26, 2019): 27771–83. http://dx.doi.org/10.1007/s11042-018-7086-y.

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27

Economopoulos, TL, PA Asvestas, GK Matsopoulos, K. Gröndahl, and H.-G. Gröndahl. "A contrast correction method for dental images based on histogram registration." Dentomaxillofacial Radiology 39, no. 5 (July 2010): 300–313. http://dx.doi.org/10.1259/dmfr/57585722.

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28

WANG, Yong, Fen-Xiong CHEN, and Hong-Xiang GUO. "Kernel-based Target Tracking with Spatial Histogram and Template Drift Correction." Acta Automatica Sinica 38, no. 3 (March 2012): 430–35. http://dx.doi.org/10.1016/s1874-1029(11)60302-9.

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29

Decarli, C., D. G. M. Murphy, D. Teichberg, G. Campbell, and G. S. Sobering. "Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity." Journal of Magnetic Resonance Imaging 6, no. 3 (May 1996): 519–28. http://dx.doi.org/10.1002/jmri.1880060316.

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30

Bilger, K., J. Kupferschlager, W. Muller-Schauenburg, F. Nusslin, and R. Bares. "Threshold calculation for segmented attenuation correction in PET with histogram fitting." IEEE Transactions on Nuclear Science 48, no. 1 (2001): 43–50. http://dx.doi.org/10.1109/23.910831.

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31

Veluchamy, Magudeeswaran, and Bharath Subramani. "Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction." Applied Soft Computing 89 (April 2020): 106077. http://dx.doi.org/10.1016/j.asoc.2020.106077.

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32

Wang, Yuwei, Jiaxu Cai, Dashan Zhang, Xiangcheng Chen, and Yajun Wang. "Nonlinear Correction for Fringe Projection Profilometry With Shifted-Phase Histogram Equalization." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–9. http://dx.doi.org/10.1109/tim.2022.3145361.

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33

Lahti, Katri, Riitta Parkkola, Päivi Jääsaari, Leena Haataja, Virva Saunavaara, Annarilla Ahtola, Mikael Ekblad, et al. "The impact of susceptibility correction on diffusion metrics in adolescents." Pediatric Radiology 51, no. 8 (April 24, 2021): 1471–80. http://dx.doi.org/10.1007/s00247-021-05000-3.

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Abstract Background Diffusion tensor imaging is a widely used imaging method of brain white matter, but it is prone to imaging artifacts. The data corrections can affect the measured values. Objective To explore the impact of susceptibility correction on diffusion metrics. Materials and methods A cohort of 27 healthy adolescents (18 boys, 9 girls, mean age 12.7 years) underwent 3-T MRI, and we collected two diffusion data sets (anterior–posterior). The data were processed both with and without susceptibility artifact correction. We derived fractional anisotropy, mean diffusivity and histogram data of fiber length distribution from both the corrected and uncorrected data, which were collected from the corpus callosum, corticospinal tract and cingulum bilaterally. Results Fractional anisotropy and mean diffusivity values significantly differed when comparing the pathways in all measured tracts. The fractional anisotropy values were lower and the mean diffusivity values higher in the susceptibility-corrected data than in the uncorrected data. We found a significant difference in total tract length in the corpus callosum and the corticospinal tract. Conclusion This study indicates that susceptibility correction has a significant effect on measured fractional anisotropy, and on mean diffusivity values and tract lengths. To receive reliable and comparable results, the correction should be used systematically.

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Huan, Zhan Hua, He Meng Yang, and Meng Zhu. "A New Method for Improving Infrared Image Quality and its System Implementation." Applied Mechanics and Materials 130-134 (October 2011): 2989–92. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2989.

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To obtain high-quality infrared image real-timely, a new correction enhancement method is proposed. The method can both compensate nonuniformity of IRFPA by using calibration-based piecewise polynomial interpolation correction algorithm and increase image contrast by using histogram-based adaptive threshold image enhancement algorithm. The experiment is performed by carrying out the method in an embedded imaging system. The results show that the system can process infrared image real-timely and the processed image is clear with high signal to noise ratio.

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Milles, Julien, Yue Min Zhu, Gérard Gimenez, Charles R. G. Guttmann, and Isabelle E. Magnin. "MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information." Computerized Medical Imaging and Graphics 31, no. 2 (March 2007): 81–90. http://dx.doi.org/10.1016/j.compmedimag.2006.11.001.

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Veluchamy, Magudeeswaran, and Bharath Subramani. "Image contrast and color enhancement using adaptive gamma correction and histogram equalization." Optik 183 (April 2019): 329–37. http://dx.doi.org/10.1016/j.ijleo.2019.02.054.

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Huang, Zhenghua, Tianxu Zhang, Qian Li, and Hao Fang. "Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images." Infrared Physics & Technology 79 (November 2016): 205–15. http://dx.doi.org/10.1016/j.infrared.2016.11.001.

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Subramani, Bharath, and Magudeeswaran Veluchamy. "Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement." Color Research & Application 45, no. 4 (April 2020): 644–55. http://dx.doi.org/10.1002/col.22502.

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Chen, Xu, and Ya Ping Zhang. "Color Calibration of Remote Sensing Imagery Based on Orthogonal Space Transformation." Applied Mechanics and Materials 20-23 (January 2010): 1315–22. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.1315.

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Due to different atmospheric conditions, seasonal changes in vegetation characteristics and other reasons, the remote sensing images captured at different time may be quite different in color, brightness and so on. In this paper, coupled with statistics classification, three common orthogonal space transformations were used to calibrate the color difference respectively. Compared with conventional methods such as the overlapping region correction and histogram matching, the results show that orthogonal transform could achieve better correction effects. The lαβ transform gets the best corrected result among three orthogonal space transform methods.

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Rashid, W., A. Hadjiprocopis, C. M. Griffin, D. T. Chard, G. R. Davies, G. J. Barker, P. S. Tofts, A. J. Thompson, and D. H. Miller. "Diffusion tensor imaging of early relapsing-remitting multiple sclerosis with histogram analysis using automated segmentation and brain volume correction." Multiple Sclerosis Journal 10, no. 1 (February 2004): 9–15. http://dx.doi.org/10.1191/1352458504ms985oa.

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Diffusion tensor magnetic resonance imaging (DTI) reveals measurable abnormalities in normal-appear ing brain tissue (NA BT) in established multiple sclerosis (MS). However, it is unclear how early this occurs. Recent studies have employed whole brain histogram analysis to improve sensitivity, but concern exists regarding reliability of tissue/cerebrospinal fluid segmentation and possible intersubject brain volume differences, which can introduce partial volume error. To address this, 28 early relapsing-remitting MS subjects [median disease duration 1.6 years; median Expanded Disability Status Scale (EDSS) score 1.5] and 20 controls were compared with whole brain histogram analysis using an automated segmentation algorithm to improve reproducibility. Brain parenchymal volumes (BPV) were estimated for each subject in the analysis. The mean, peak height and peak location were calculated for DTI parameters [fractional anisotropy (FA), mean diffusivity and volume ratio]. A n increased FA peak height in MS subject NA BT was observed (P =0.02) accounting for age, gender and BPV. Removing BPV revealed additional abnormalities in NABT. The main conclusions are i) FA peak height is increased in NA BT in early MS, ii) partial volume edge effects may contribute to apparent NA BT histogram abnormalities, and iii) correction for brain volume differences should reduce potential partial volume edge effects.

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Bianco, G., M. Muzzupappa, F. Bruno, R. Garcia, and L. Neumann. "A NEW COLOR CORRECTION METHOD FOR UNDERWATER IMAGING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W5 (April 9, 2015): 25–32. http://dx.doi.org/10.5194/isprsarchives-xl-5-w5-25-2015.

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Recovering correct or at least realistic colors of underwater scenes is a very challenging issue for imaging techniques, since illumination conditions in a refractive and turbid medium as the sea are seriously altered. The need to correct colors of underwater images or videos is an important task required in all image-based applications like 3D imaging, navigation, documentation, etc. Many imaging enhancement methods have been proposed in literature for these purposes. The advantage of these methods is that they do not require the knowledge of the medium physical parameters while some image adjustments can be performed manually (as histogram stretching) or automatically by algorithms based on some criteria as suggested from computational color constancy methods. One of the most popular criterion is based on gray-world hypothesis, which assumes that the average of the captured image should be gray. An interesting application of this assumption is performed in the Ruderman opponent color space lαβ, used in a previous work for hue correction of images captured under colored light sources, which allows to separate the luminance component of the scene from its chromatic components. In this work, we present the first proposal for color correction of underwater images by using lαβ color space. In particular, the chromatic components are changed moving their distributions around the white point (white balancing) and histogram cutoff and stretching of the luminance component is performed to improve image contrast. The experimental results demonstrate the effectiveness of this method under gray-world assumption and supposing uniform illumination of the scene. Moreover, due to its low computational cost it is suitable for real-time implementation.

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Wang, Gui Zhou, and Guo Jin He. "A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image." Advanced Materials Research 341-342 (September 2011): 893–97. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.893.

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The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

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Harichandana, M., V. Sowmya, V. V. Sajithvariyar, and R. Sivanpillai. "COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 45–50. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-45-2020.

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Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.

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Elaraby, Ahmed, and Ayman Taha. "A Framework for Cross-Modality Guided Contrast Enhancement of CT Liver Using MRI." Traitement du Signal 38, no. 6 (December 31, 2021): 1671–75. http://dx.doi.org/10.18280/ts.380610.

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In liver medical imaging, physicians always detect, monitor, and characterize liver diseases by visually assessing of liver medical images. Computed Tomographic (CT) imaging is considered as one of the efficient medical imaging modalities in diagnosis of various human diseases. However, imprecise visualization and low contrast are the drawbacks that limit its utility. In this paper, a novel approach of multimodal liver image contrast enhancement is proposed. The idea behind the proposed approach is utilizing MRI scan as guide to exploit the diversity information extracted to enhance the structures in CT modal of liver. The proposed enhancement technique consists of two phases of enhancement to assess local contrast of the input images. In the first phase, the two image modalities are converted to the same range as the range of MRI and CT are different. Then, we did transformation of CT image so that its histogram matches the histogram of MRI. Second, the adaptive gamma correction-based histogram modification is utilized to get enhanced CT image. The subjective and objective experimental results indicated that the proposed scheme generates significantly enhanced liver CT.

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Román, Julio César Mello, Vicente R. Fretes, Carlos G. Adorno, Ricardo Gariba Silva, José Luis Vázquez Noguera, Horacio Legal-Ayala, Jorge Daniel Mello-Román, Ricardo Daniel Escobar Torres, and Jacques Facon. "Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology." Sensors 21, no. 9 (April 29, 2021): 3110. http://dx.doi.org/10.3390/s21093110.

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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

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Hanji, Dr Geeta. "Deep Learning Approach for Submerged Image Enhancement." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 214–19. http://dx.doi.org/10.22214/ijraset.2021.38391.

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Abstract: Because of underwater pictures application in ocean engineering, ocean research, marine biology, and marine archaeology to name a few, underwater picture enhancement was widely publicized in the last several years. Underwater photos frequently upshot in low contrast, blurred, color distortion, hazy, poor visible images. This is because of light attenuation, absorption, scattering (forward scattering and backward scattering), turbidity, floating particles. As a result, effective underwater picture solution must be developedin order to improve visibility, contrast, and color qualities for greater visual quality and optical attractiveness. Many underwater picture enhancing approaches have been proposed to overcome these challenges; however they all failed to produce accurate results. Hence for this we first undertook a large scale underwater image dataset which is trained by convolution neural network (CNN) and then we have studied and implemented a deep learning approach called very deep super resolution (VDSR) model for improving the color, contrast, and brightness of underwater photos by using different algorithms such as white balance, histogram equalization, and gamma correction respectively. Moreover, our method is compared with the existing method which reveals that our method surpassesthe existing methods Keywords: CNN, gamma correction, histogram equalization, underwater image enhancement, VDSR, white balance

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Baguda, Yakubu Suleiman, Abubakar Suleiman Baguda, and Usman Suleiman Baguda. "Intensity Inhomogeneity Correction Scheme for 3d-Dimensional Mri Brain Scans using Histogram Matching." VAWKUM Transactions on Computer Sciences 11, no. 2 (December 20, 2016): 1. http://dx.doi.org/10.21015/vtcs.v11i2.435.

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Wang, Liqun, H.-Ming Lai, Gareth J. Barker, David H. Miller, and Paul S. Tofts. "Correction for variations in MRI scanner sensitivity in brain studies with histogram matching." Magnetic Resonance in Medicine 39, no. 2 (February 1998): 322–27. http://dx.doi.org/10.1002/mrm.1910390222.

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Chung, Seyoung, Sun Mi Choi, Wook Lee, Kwang Hyun Cho, and Young Min Rhee. "Free energy level correction by Monte Carlo resampling with weighted histogram analysis method." Chinese Journal of Chemical Physics 33, no. 2 (April 2020): 183–95. http://dx.doi.org/10.1063/1674-0068/cjcp2001001.

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Zhou, Jingchun, Lei Pang, and Weishi Zhang. "Underwater image enhancement method based on color correction and three-interval histogram stretching." Measurement Science and Technology 32, no. 11 (August 19, 2021): 115405. http://dx.doi.org/10.1088/1361-6501/ac16ef.

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