A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement

Singh, Himanshu; Kumar, Anil; Balyan, L. K.; Singh, Girish Kumar

doi:10.1016/j.compeleceng.2017.11.014

Cited by 37 publications

(13 citation statements)

References 15 publications

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“…A histogram planting based contrast enhancement is presented in [9]. Gamma correction is yet another contrast enhancement technique which is a non-linear technique and modifies the individual image pixels [10]. Various modifications of gamma correction are also done to improve the performance of this method [11].…”

Section: Methodsmentioning

confidence: 99%

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

et al. 2020

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The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues in satellite image processing. This paper proposes an optimized contrast stretching using non-linear transformation for image enhancement. The non-linear transformation is influenced by the appropriate choice of control parameters for the sample images since manual tuning for individual images is tedious. A Bat algorithm based tuning is employed for the automated selection of control parameters in the transformation. The performance of the optimization algorithm is compared against other metaheuristic algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). It is noted that the bat algorithm based contrast enhancement outperforms the other optimization techniques in terms of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy and CPU time (Central Processing Unit).

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Section: Methodsmentioning

confidence: 99%

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

et al. 2020

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“…This, in turn, results into restriction of oversaturated as well as sub-enhanced patches and hence, the objective of more and more information exploration get fulfilled. When intensity values of the input image (I in ) are normalized from zero to unity, if they are exponentially employed by a gamma value more than unity, it leads to intensity compression and hence, can be termed as gamma compressed intensity channel (I gcp ) as [2], [3]:…”

Section: A Piecewise Gamma Correctionmentioning

confidence: 99%

“…This leads to counter correction for dark patches and also for boosting up for less illuminated image-patches. The interim gamma corrected expanded (I gex ) channel can be evaluated as [2], [3]:…”

Section: A Piecewise Gamma Correctionmentioning

confidence: 99%

“…Fractional-order 1-D filtering can be extended to a 2-D image matrix, and hence, a set of fractional-order partial differential equations w.r.t. x and y-direction can be expressed as [3]: that sum of all elements remains unity. Masks of 3x3 and 7x7 size have been also tested for this, but a trade-off is settled for 5x5 size.…”

Section: Proposed Pgcrlfoim Frameworkmentioning

confidence: 99%

“…Contemporary advancements in all technological spheres for social welfare cannot be imagined without signals and their ''on-demand or application-specific ''contextual processing, in one form or the other. A large amount of work has already been focused on image enhancement through FO differentiation based masking [3]. More robustness can be imparted for pre-existing methodologies by an organized and efficient involvement of swarm intelligence in association with the fractionaldifferential approach.…”

Section: Introductionmentioning

confidence: 99%

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Fractional-Order Integration Based Fusion Model for Piecewise Gamma Correction Along With Textural Improvement for Satellite Images

et al. 2019

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Fractional-order integration (FOI) and its beauty of optimally ordered adaptive filtering for image quality enhancement are latently too valuable to be casually dismissed. With this motivation, a new Riemann-Liouville fractional-order calculus-based spatial-masking methodology is proposed in this paper in association with counterbalanced piecewise gamma correction (PGC). A generalized FOI-based mask is also suggested. This mask is negatively augmented with the original image for harvesting texture-based benefits. PGC is just employed through a constructive association of both kinds of reciprocally dual gamma values (γ 1 = γ and γ 2 = 1/γ , ∀γ > 1), which leads to optimally desired enhancement when applied in a weighted counter-correction manner. Efficiently improved and recently proposed opposition-based learning inspired sine-cosine algorithm is employed in this paper, along with a newly framed fitness function. This fitness function is devised in a novel manner by taking care of textural as well as non-textural details of the images. In this paper, especially for dark images, 130% increment is achieved over the input contrast along with the simultaneous 147% increment in the discrete entropy level and 22.8% increment in the sharpness content. Also, brightness and colorfulness are reported with 130% and 196.4% increased with respect to the input indices, respectively. In addition, the textural improvement is advocated in terms of desired comparative reduction of gray-level co-occurrence matrix-based metrics, namely, correlation, energy, and homogeneity, which are suppressed by 25.6%, 72.5%, and 21.8%, respectively. This performance evaluation underlines the excellence and robustness for imparting proper texture as well as edge preserved (or efficiently restored) image quality improvement. INDEX TERMSFractional-order (FO) masking filter, fractional-order integration (FOI), Riemann-Liouville (RL) definition, sine cosine algorithm (SCA), opposition-based learning (OBL), gray-level co-occurrence matrix (GLCM), quality enhancement, optimal mask designing, two-dimensional (2-D) adaptive filtering, piecewise-gamma correction (PGC).

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Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images

Singh

Kumar

Balyan

2020

Applications of Hybrid Metaheuristic Algorithms for Image Processing

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A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement

Cited by 37 publications

References 15 publications

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

Fractional-Order Integration Based Fusion Model for Piecewise Gamma Correction Along With Textural Improvement for Satellite Images

Hybrid Grey-Wolf Optimizer Based Fractional Order Optimal Filtering for Texture Aware Quality Enhancement for Remotely Sensed Images

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