Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Tirumani, V Hyma Lakshmi; Tenneti, Madhu; K, Ch. Srikavya; Kotamraju, Sarat K.

doi:10.1049/ipr2.12268

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…This framework first generates multiple artificially multi-exposure images using a mapping function, then combines exposure to create a weight map, and finally fuses different frequency bands of the image. Testing outcomes showed that this method outperforms existing algorithms in enhancing low-light images [9]. Lu et al believed that the current image enhancement methods based on convolutional neural network models do not differentiate image features on different channels, which hinders the learning of hierarchical features.…”

Section: Literature Reviewmentioning

confidence: 98%

“…However, due to the limitations of low-light environments, Chinese paintings in low-light scenes may face the problem of uneven pigment brightness, which affects the visibility and artistic quality of the artwork to some extent. A low-light environment refers to an environment with dim lighting or insufficient light sources [2]. In such environments, due to the scarcity and weakening of light, details in the image are difficult to display clearly, and the differences in pigment brightness become more pronounced.…”

Section: Introductionmentioning

confidence: 99%

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Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Cheng

2024

IJACSA

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With the promotion and development of Chinese painting and the advancement of photography technology, people can appreciate various types of Chinese paintings through image and other methods. However, Chinese painting images in low-light environments face the problem of extreme uneven brightness distribution. The currently proposed solutions for this problem are not sufficient. Therefore, this research proposes a brightness equalization algorithm for Chinese painting pigments in low-light environments based on region division. This algorithm also utilizes guided filtering for image denoising. In performance testing, the proposed method has a runtime of 16.63 seconds under a scaling factor of 1 and a runtime of 8.37 seconds under a scaling factor of 0.1, which are the fastest among the compared algorithms. In simulation experiments, the brightness equalization value of the proposed method is 198.93, which is listed at the best among all the compared algorithms. This research provides a valuable research direction for the brightness equalization of Chinese painting pigments.

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Section: Literature Reviewmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Cheng

2024

IJACSA

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“…After 𝐽-level decomposition of the micro-animation video image, 8𝐽 + 1 sub-images will be obtained, that is, the expression of the image in the wavelet domain is [12] :…”

Section: Image Denoising Processingmentioning

confidence: 99%

High-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environment

Su,

Djenouri

2023

Preprint

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In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image feature matching. This article designs a high-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environments. Fully denoise micro animation video images using 2D High Density Discrete Wavelet Transform (HD-DWT), and apply fixed block count segmentation to process micro animation video images; Using Harris algorithm to complete image corner detection and obtain corner features of sub images; In the K-means clustering algorithm, SIFT feature vectors are divided into clusters and paired with the nearest neighbor cluster in another sub image to form a sub image matching pair, completing block based sub image matching; Combine all sub image matching results to obtain video image matching results, and use the Improved Random Sampling Consistency (RANCAS) algorithm to remove incorrect matching during the matching process, improving matching accuracy. The experimental results show that the designed algorithm can effectively reduce image noise, improve image quality, and generate a large number of matching pairs in mobile network environments. After the application of the designed algorithm, the production effect of micro animated videos in mobile networks can be significantly improved.

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“…Luque_Chang et al 30 have introduced Moth Swarm Algorithm (MSA) for image contrast enhancement. Other optimization algorithms have also been used such as Artificial bee colony (ABC), 5 Barnacles Mating Optimizer (BMO), 2 Genetic algorithm (GA), 1 Social spider optimization algorithm (SSOA), 33 and Particle swarm optimization (PSO) 53 …”

Section: Introductionmentioning

confidence: 99%

“…Other optimization algorithms have also been used such as Artificial bee colony (ABC), 5 Barnacles Mating Optimizer (BMO), 2 Genetic algorithm (GA), 1 Social spider optimization algorithm (SSOA), 33 and Particle swarm optimization (PSO). 53 The MPA 15 was a recently proposed meta-heuristic algorithm. So far, this algorithm has been effectively applied in many applications, such as medical image synthesis, 9 multilevel image segmentation, 32 and fault diagnosis of rolling bearing.…”

mentioning

confidence: 99%

A new medical image enhancement algorithm using adaptive parameters

Dinh

Giang

2022

Int J Imaging Syst Tech

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The quality of medical images plays a vital role in many image processing applications such as image segmentation, feature extraction, image classification, image recognition, and image fusion. Some of the common problems with Medical images are noise, blur, or low contrast. According to our observations, current image enhancement algorithms only focus on solving individual problems such as gray level adjustment, noise reduction, or sharpness enhancement. This paper proposes a novel algorithm to solve problems on images simultaneously. First, we propose an image decomposition algorithm. This algorithm allows decomposing the image into three components: structure (IS), texture (IT), and noise (IN). Second, the structural component (IS) is enhanced by the contrast‐limited adaptive histogram equalization method to obtain ICLAHE. We use the structure tensor salient detection operator and the Laplace edge detection operator to add structural and texture features. These operators are applied to the ICLAHE and IT components to obtain the ISTS and ILED components, respectively. The IT and ILED components are used to generate the enhanced component (called IT_E) by using the Max operator. Third, the Marine predators algorithm is used to find the optimal parameters β1, β2, β3, and β4 corresponding to ICLAHE, ISTS, IT_E, and IN. Finally, the enhanced image is made up of the sum of the ICLAHE, ISTS, IT_E, and IN images multiplied by the optimal parameters β1, β2, β3, and β4, respectively. Six state‐of‐the‐art image enhancement approaches, seven medical image fusion algorithms, and six image quality metrics have been utilized to verify the proposed approach's effectiveness. The experimental results show that the proposed method significantly improves the quality of the input medical images as well as significantly improves the efficiency of current medical image synthesis algorithms.

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Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Cited by 9 publications

References 12 publications

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

Brightness Equalization Algorithm for Chinese Painting Pigments in Low-Light Environment Based on Region Division

High-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environment

A new medical image enhancement algorithm using adaptive parameters

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