Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction

Veluchamy, Magudeeswaran; Subramani, Bharath

doi:10.1016/j.asoc.2020.106077

Cited by 46 publications

(17 citation statements)

References 19 publications

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“…Moreover, Corona imagery, combined with more recent satellite imagery, allows for the creation of extended decadal time-scale series, as the one demonstrated by Tappan and McGahuey [ 15 ] who compiled a study from 1965 until 2001 in Southern Mali. The findings of the current study indicate research gaps that could become active fields of future research, such as the deployment of Corona for agricultural mapping (since the high spatial resolution is a per-requisite for smallholder agriculture which prevailed during this historic time), advanced segmentation, and classification schemes (e.g., [ 76 , 77 ]), application of fuzzy methods to compensate for imagery inconsistencies (e.g., [ 34 , 35 ]), and radiometric comparison with contemporary VHSR similar satellite systems (e.g., Quickbird, Ikonos, WorldView).…”

Section: Discussionmentioning

confidence: 96%

“…It is perhaps these technical challenges that have discouraged widespread sophisticated use of this unprecedented historic archive and use it instead simply as a reference for visual interpretation. Moreover, quality inconsistency across images and mainly across missions, appeals for advanced pre-processing techniques for the inaccuracies and uncertainties such as fuzzy methods for gray-level image contrast enhancement (e.g., [ 34 , 35 ]).…”

Section: Introductionmentioning

confidence: 99%

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Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s

Stratoulias

Grekousis

2021

Sensors

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The Corona satellite program was a historic reconnaissance mission which provided high spatial resolution panchromatic images during the Cold War era. Nevertheless, and despite the historic uniqueness and importance of the dataset, efforts to extract tangible information from this dataset have primarily focused on visual interpretation. More sophisticated approaches have been either hampered or unrealized, often justified by the primitive quality of this early satellite product. In the current study we attempt to showcase the usability of Corona imagery outside the context of visual interpretation. Using a 1968 Corona image acquired over the city municipality of Plovdiv, Bulgaria, we reconstruct a panchromatic 1.8 m spatial resolution georegistered image with a relative displacement Root Mean Square Error (RMSE) of 6.616 (for x dimension) and 1.886 (for y dimension) and employ segmentation and texture analysis to discern agricultural parcels and settlements’ footprints. Population statistics of this past era are retrieved from national census and related to settlements’ footprints. An exponential relationship between the two variables was identified by applying a semi-log regression. The high adjusted R2 value found (76.54%) indicates that Corona images offer a unique opportunity for population data analysis of the past. Overall, we showcase that the Corona images’ usability extends beyond the visual interpretation, and features of interest extracted through image analysis can be subsequently used for further geographical and historical research.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s

Stratoulias

Grekousis

2021

Sensors

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“…al. [29] presented a Fuzzy Dissimilarity Adaptive Histogram Equalization with Gamma Correction (FDAHE-GC) algorithm in which a Fuzzy Dissimilarity Histogram (FDH) is obtained from the neighbourhood characteristics forming intensity mapping function. Different methods for evaluating the performance of the presented method include entropy, Colorfulness, Hue Deviation Index, Saturating, Contrast Enhancement Factor, and Gradient which show improved color enhancement.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Dixit¹,

Yadav²,

Mishra³

2021

IJACSA

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Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image. Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts. In this work, we overcome the shortcomings of both the local color correction and global color correction. This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images. Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images. Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques. Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed. The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.

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“…Images can provide feature information such as texture, morphology, and color for CNNs. When extracting features from images, images are always affected by various uncertain factors [16][17][18]. In order to reduce the impact of uncertainty, researchers use some data enhancement methods [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Feature Fusion Method with Guided Training for Classification Tasks

Zhang

Fan

et al. 2021

Computational Intelligence and Neuroscience

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In this paper, a feature fusion method with guiding training (FGT-Net) is constructed to fuse image data and numerical data for some specific recognition tasks which cannot be classified accurately only according to images. The proposed structure is divided into the shared weight network part, the feature fused layer part, and the classification layer part. First, the guided training method is proposed to optimize the training process, the representative images and training images are input into the shared weight network to learn the ability that extracts the image features better, and then the image features and numerical features are fused together in the feature fused layer to input into the classification layer for the classification task. Experiments are carried out to verify the effectiveness of the proposed model. Loss is calculated by the output of both the shared weight network and classification layer. The results of experiments show that the proposed FGT-Net achieves the accuracy of 87.8%, which is 15% higher than the CNN model of ShuffleNetv2 (which can process image data only) and 9.8% higher than the DNN method (which processes structured data only).

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Fuzzy dissimilarity color histogram equalization for contrast enhancement and color correction

Cited by 46 publications

References 19 publications

Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s

Information Extraction and Population Estimates of Settlements from Historic Corona Satellite Imagery in the 1960s

Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

A Feature Fusion Method with Guided Training for Classification Tasks

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