Department of Computer Science

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    ULIE: Underwater Low-Light Image Enhancement Using Deep Learning
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Umer; CIIT/SP24-RCS-018/LHR; Dr. Muhammad Aksam Iftikhar Associate Professor, 55P; LHR TP 10040
    Among the Underwater images captured in low-light conditions are usually suffer from severe quality degradation related to insufficient illumination, wavelength-dependent colour attenuation, scattering and noise amplification, while strongly affecting the applicability in underwater vision applications. Correcting these problems while preserving computational efficiency is critical in real-time systems and systems with limited resources underwater. This thesis introduces an efficient deep learning based approach for underwater low-light image enhancement to focus on image improvement in terms of illuminating effect, structural details preservation and color fidelity restoration. This thesis presents light-weight enhancement approach to ensure low computational complexity and a composite loss function is designed to help guide the reconstruction in terms of pixel-level reconstruction, structural similarity, perceptual consistency and color-correction. The proposed method is end-to-end trained and tested on the EUVP Dark dataset by using quantitative and qualitative evaluations. Experimental results show that the proposed approach is able to get significant improvement in illumination recovery and reconstruction accuracy, and the corresponding PSNR value of 28.52, SSIM value of 0.8433 and UIQM value of 2.79. Qualitative analysis also shows increased visibility, balanced color restoration and increased detail clarity in severely degraded underwater images. These results indicate that combining an efficient network design and a task-specific formulation of loss is a good solution for underwater low-light image enhancement, especially for real-time deployment scenarios.
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    AI for Media Fairness: Detecting Bias in English News
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Fozia Mujahid; CIIT/SP24-RCS-002/LHR; Dr. Abid Sohail Bhutta; LHR TP 10035
    The digital journalism growth has enabled many more people to have access to a larger amount of information and news; however, this increased access to the internet and news has also brought attention to an increasing number of additional concerns regarding the influence of bias on how news media report, produce and share their content [1]. In this thesis, we explore and research multiple forms of bias found within English language based media and offer an AI-powered solution to identifying and classifying bias within the English language media [2]. The focus of our research is the major forms of media bias – i.e., (i) language bias; (ii) selection bias; (iii) framing bias; (iv) sentiment bias; and (v) ideological bias. The main data source used in this research is a publically available data corpus known as the Media Bias Identification Corpus (MBIC) which consists of labeled media articles written in the English language. We then utilize Natural Language Processing (NLP) techniques to preprocess the text and to obtain features, and subsequently apply several different types of machine learning and deep learning models to classify media biased content [3]. We thoroughly evaluate the performance of all of the trained models using standard performance metrics in order to compare their classification performance, as well as to apply topic modeling to evaluate how media topics are associated with particular forms of bias. Our evaluation of the experimental results found that framing bias and sentiment-based forms of bias were the two most prevalent forms of bias found within media articles written in English. Finally, the machine learning, and more specifically the deep learning and transformer based, models outperformed models for the purpose of detecting media bias in English written articles. The results of our research will aid in developing a fair and transparent approach to digital journalism for media organizations, by providing insight into how the ty