Swarm intelligent based contrast enhancement algorithm with improved visual perception for color images

Kanmani, Madheswari; Venkateswaran, N.

doi:10.1007/s11042-017-4911-7

Cited by 24 publications

(5 citation statements)

References 31 publications

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“…According to the description in [ 40 ], the evaluation methods of image quality can be divided into two categories: subjective and objective. The only subjective evaluation is the Mean Opinion Score (MOS), which means that many people judge the quality of an image, and the average of their scores is used as the evaluation result.…”

Section: Resultsmentioning

confidence: 99%

A color image contrast enhancement method based on improved PSO

et al. 2023

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Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle’s flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

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Section: Resultsmentioning

confidence: 99%

A color image contrast enhancement method based on improved PSO

et al. 2023

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“…This algorithm introduced a cuckoo search algorithm and a bilateral gamma adjustment function in the HIS color space to improve the overall brightness of the image. Kanmani and Narasimhan [ 19 ] established a population intelligence-based color image contrast enhancement algorithm that uses an adaptive gamma correction factor selected by a particle swarm algorithm (PSO) to improve the image entropy and enhance the image details. Li et al [ 20 ] proposed an adaptive chaotic particle swarm optimization algorithm (ACPSO) combined with gamma correction to iteratively find the best image for global brightness adjustment.…”

Section: Related Workmentioning

confidence: 99%

“…where the pixel average and standard deviation of the guidance image in the window ω k with radius r and central pixel k are µ k and σ k , pk is the mean of the filtering image in the window ω k , and N ω k is the total number of pixels in the window ω k . Bring Equations ( 20) and ( 21) into Equations ( 18) and (19).…”

Section: Edge Enhancementmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Yan,

Qin,

Zhang

et al. 2023

Entropy

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Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

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“…In order to solve the above problems, in recent years, many researchers have developed different swarm intelligence (SI) optimization algorithms for image enhancement, such as particle swarm optimization algorithm [14], and immune algorithm [15]. Table 1 summarizes several newly published related works.…”

Section: Related Workmentioning

confidence: 99%

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Xiong

Zhang

et al. 2021

Mathematics

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At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

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Swarm intelligent based contrast enhancement algorithm with improved visual perception for color images

Cited by 24 publications

References 31 publications

A color image contrast enhancement method based on improved PSO

A color image contrast enhancement method based on improved PSO

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

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