Adaptive Fractional Image Enhancement Algorithm Based on Rough Set and Particle Swarm Optimization

Zhang, Xuefeng; Liu, Ri; Ren, Jianxu; Gui, Qinglong

doi:10.3390/fractalfract6020100

Cited by 23 publications

(21 citation statements)

References 35 publications

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“…Zhang constructed a new image-enhancement algorithm based on a rough set and a fractional-order differentiator. An image enhanced by this algorithm has a clear edge and rich textural details, and it can retain information from the smooth areas in an image [12,13].…”

Section: Introductionmentioning

confidence: 99%

Depth Image Enhancement Algorithm Based on Fractional Differentiation

et al. 2023

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Depth image enhancement techniques can help to improve image quality and facilitate computer vision tasks. Traditional image−enhancement methods, which are typically based on integer−order calculus, cannot exploit the textural information of an image, and their enhancement effect is limited. To solve this problem, fractional differentiation has been introduced as an innovative image−processing tool. It enables the flexible use of local and non−local information by taking into account the continuous changes between orders, thereby improving the enhancement effect. In this study, a fractional differential is applied in depth image enhancement and used to establish a novel algorithm, named the fractional differential–inverse−distance−weighted depth image enhancement method. Experiments are performed to verify the effectiveness and universality of the algorithm, revealing that it can effectively solve edge and hole interference and significantly enhance textural details. The effects of the order of fractional differentiation and number of iterations on the enhancement performance are examined, and the optimal parameters are obtained. The process data of depth image enhancement associated with the optimal number of iterations and fractional order are expected to facilitate depth image enhancement in actual scenarios.

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Section: Introductionmentioning

confidence: 99%

Depth Image Enhancement Algorithm Based on Fractional Differentiation

et al. 2023

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“…The simulated annealing algorithm jumps out from the local extrema to find the global extrema. Zhang et al 31 . combined rough sets with PSO algorithms to distinguish between smooth, edge, and texture regions of images.…”

Section: Introductionmentioning

confidence: 99%

“…The simulated annealing algorithm jumps out from the local extrema to find the global extrema. Zhang et al 31 combined rough sets with PSO algorithms to distinguish between smooth, edge, and texture regions of images. These algorithms have achieved good results in the fields of image segmentation, image recognition, and image fusion.…”

Section: Introductionmentioning

confidence: 99%

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

2023

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The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.

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“…For image filtering, setting the filter as a fractional-order filter gives more freedom. Zhang et al proposed an adaptive fractional differential filter based on a rough set and particle swarm optimization (PSO) algorithm [8], enhanced by which images retain a clear edge and rich texture details. Fractional-order derivatives can also be applied to multi-focus image fusion to reflect the clarity of the image; this method has been experimentally proven to outperform the state-of-the-art methods for multi-focus image fusion [9].…”

Section: Introductionmentioning

confidence: 99%

Frac-Vector: Better Category Representation

Tan

2023

Fractal Fract

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For this paper, we proposed the fractional category representation vector (FV) based on fractional calculus (FC), of which one-hot label is only the special case when the derivative order is 0. FV can be considered as a distributional representation when negative probability is considered. FVs can be used either as a regularization method or as a distributed category representation. They gain significantly in the generalization of classification models and representability in generative adversarial networks with conditions (C-GANs). In image classification, the linear combinations of FVs correspond to the mixture of images and can be used as an independent variable of the loss function. Our experiments showed that FVs can also be used as space sampling, with fewer dimensions and less computational overhead than normal distributions.

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Adaptive Fractional Image Enhancement Algorithm Based on Rough Set and Particle Swarm Optimization

Cited by 23 publications

References 35 publications

Depth Image Enhancement Algorithm Based on Fractional Differentiation

Depth Image Enhancement Algorithm Based on Fractional Differentiation

Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

Frac-Vector: Better Category Representation

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