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  • Open Access


    Gamma Correction for Brightness Preservation in Natural Images

    Navleen S Rekhi1,2,*, Jagroop S Sidhu2, Amit Arora2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2791-2807, 2023, DOI:10.32604/csse.2023.026976

    Abstract Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning… More >

  • Open Access


    Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

    Ayat Alrosan1, Waleed Alomoush2, Mohammed Alswaitti3,*, Khalid Alissa4, Shahnorbanun Sahran5, Sharif Naser Makhadmeh6, Kamal Alieyan7

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1575-1593, 2021, DOI:10.32604/cmc.2021.015925

    Abstract Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its… More >

  • Open Access


    Text Detection and Classification from Low Quality Natural Images

    Ujala Yasmeen1, Jamal Hussain Shah1, Muhammad Attique Khan2, Ghulam Jillani Ansari1, Saeed ur Rehman1, Muhammad Sharif1, Seifedine Kadry3, Yunyoung Nam4,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1251-1266, 2020, DOI:10.32604/iasc.2020.012775

    Abstract Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image.… More >

  • Open Access


    Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey

    Bingtao Hu*, Jinwei Wang

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 95-105, 2020, DOI:10.32604/jihpp.2020.010464

    Abstract With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision. This rendered image is invisible to the naked eye. How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics. In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem. This paper aims to track the latest developments and applications of deep learning in More >

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