English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In an underwater view, wavelength-reliant light absorption in addition to scattering worsens the visibility of images that lead to lower contrast and also distorted color casts. To handle these problems, a proficient approach is proposed for the enhancement and also for the classification of UI. Originally, the underwater input image RGB is modified using Enhanced ABC algorithm. The subsequent phase is to extract the features. The extracted attributes are inputted to the Modified PCA approach, in this phase, the dimensionality of the features is reduced. Then, the classification operation is performed by utilizing ANFIS classifier. At last, the classified enhanced deeper water images along with the enhanced shallow water images are analyzed during the testing phase. The performance analysis is made for the proposed classifiers and existing techniques such as NN, SVM, and KNN. In addition, the performance of the proposed EABC model is compared over ABC, GA, and PSO in terms of correlation, Spearman rank correlation, sharpness, EME, Mutual information and NMI. The proposed classified method obtain the accuracy (0.9473), sensitivity (0.9230), specificity (0.9677) however, the existing methods provide only 0.8771 accuracy. Similarly, the proposed Enhanced ABC methods provide the improved performance while considering the other optimization algorithm. Abbreviations:  ABC: artificial bee colony; ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; CM: covariance matrix; DWT: discrete wavelet transform; EABC: enhanced artificial bee colony; EME: enhanced measurement error; FDR: false discovery rate; FLS: forward-looking sonar; FPR: false positive rate; FS: forward scattering; GA: genetic algorithm; KNN: k-nearest neighbors; MCC: Mathew’s correlation coefficient; MOE: measure of entropy; NMI: normalized mutual information; NN: neural network; NPV: negative prediction value; PCA: principal component analysis; PD: probability distribution; PSO: particle swarm optimization; RGB: red green blue; SD: standard deviation; SRC: Spearman rank correlation; SURF: speeded up robust feature; SVM: support vector machine; SWT: stationary wavelet transform; UIE: underwater image enhancement; WVD: Wigner-Ville distribution

Enhanced artificial bee colony approach for the enhancement and classification of underwater images