Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement

Kuran, Umut; Kuran, Emre Can

doi:10.1016/j.iswa.2021.200051

Cited by 32 publications

(16 citation statements)

References 32 publications

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“…While histogram equalization may make undesired image noises more visible (since it does not adjust to local contrast requirements, small contrast differences can go unnoticed when a given gray range has a large number of pixels), it is a straightforward and user-friendly method for evenly distributing the image's intensity levels [28] , [29] . The CLAHE solves the noise issue by introducing a clip limit.…”

Section: Methodsmentioning

confidence: 99%

On the search for efficient face recognition algorithm subject to multiple environmental constraints

Essel,

Mensah,

Ocran

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

On the search for efficient face recognition algorithm subject to multiple environmental constraints

Essel,

Mensah,

Ocran

et al. 2024

Heliyon

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“…CLAHE splits the image into MxN local tiles. For all the tiles, histograms can be individually calculated [17]. First, we should compute the average amount of pixels for each region to computer the histogram as follows:…”

Section: Image Pre-processingmentioning

confidence: 99%

Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

Sharmili¹,

Yonbawi²,

Alahmari³

et al. 2023

Computer Systems Science and Engineering

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Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization with an improved SqueezeNet-based FER (EWOISN-FER) model. The presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization (CLAHE) technique as a pre-processing step. In addition, the improved SqueezeNet model is exploited to derive an optimal set of feature vectors, and the hyperparameter tuning process is performed by the stochastic gradient boosting (SGB) model. Finally, EWO with sparse autoencoder (SAE) is employed for the FER process, and the EWO algorithm appropriately chooses the SAE parameters. A wide-ranging experimental analysis is carried out to examine the performance of the proposed model. The experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

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“…The filters used to retrieve features from images include CLAHE (contrast limited adaptive histogram equalization) [24], Gabor filter [25], Gamma Correction [26], Gaussian filter [27], Hessian [8], Laplacian operator [28], Median filter [29], Mean filter [30], Minimum filter [31], Bilateral filter [32], Sobel operator [33], Canny edge detector [34], as well as the ten filters predefined in the imageFilter module of Pillow [35], which are BLUR, CONTOUR, DE-TAIL, EDGE ENHANCE, EDGE ENHANCE MORE, EMBOSS, FIND EDGES, SMOOTH, SMOOTH MORE and SHARPEN. The mathematical definitions of these filters/operators are shown in Table 4.…”

Section: Feature Transformationmentioning

confidence: 99%

Compensating small data with large filters for accurate liver vessel segmentation

Chen

Zhao

Bian

et al. 2023

Preprint

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Background: Segmenting liver vessels on computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. Methods: We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. Results: In experiments on the well-known dataset 3D-IRCADb, the accuracy is improved to 0.99, and the averaged Dice coefficient is lifted to 0.63. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. Conclusion: Sophisticated integration of large number filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.

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Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement

Cited by 32 publications

References 32 publications

On the search for efficient face recognition algorithm subject to multiple environmental constraints

On the search for efficient face recognition algorithm subject to multiple environmental constraints

Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

Compensating small data with large filters for accurate liver vessel segmentation

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