Objective

This project focuses on the design and implementation of an advanced image super-resolution system to enhance the quality of vintage TV covers on the Orange TV platform. By leveraging state-of-the-art deep learning techniques (Real-ESRGAN, SwinFIR, HAT, etc.) alongside hyperparameter-optimization tools (Optuna), fine-tuning, and LoRA, it aims to automatically restore and upscale low-resolution images, balancing visual fidelity, perceived quality, and computational efficiency.

This project focuses on the design and implementation of an advanced image super-resolution system to enhance the quality of vintage TV covers on the Orange TV platform. By leveraging state-of-the-art deep learning techniques (Real-ESRGAN, SwinFIR, HAT, etc.) alongside hyperparameter-optimization tools (Optuna), fine-tuning, and LoRA, it aims to automatically restore and upscale low-resolution images, balancing visual fidelity, perceived quality, and computational efficiency.

The specific objectives are:

● Compare super-resolution architectures (Real-ESRGAN, SwinFIR, HAT, etc.) by evaluating PSNR, SSIM, MS-SSIM, FSIM, and no-reference metrics (PIQE).

● Optimize hyperparameters at inference with Optuna to maximize perceptual quality without sacrificing fidelity.

● Fine-tune Real-ESRGAN on proprietary vintage cover data with typical distortions (resizing, noise, blur, compression).

● Integrate LoRA to add low-rank adapter layers and speed up training iterations.

● Define an evaluation protocol including inference-time analysis, standard deviation across multiple distortions, and scalability to large collections.

BACHELOR’S THESIS BY:

NATALIA RODRÍGUEZ NAVARRO

Academic Experience

  • Computer Science and Engineering, Universidad Carlos III de Madrid (September 2021 – September 2025)

     

    Work Experience

    • AI researcher assistant – Universidad Carlos III de Madrid in collaboration with Grupo MasOrange (October 2024 — June 2025)
    • Computer Science and Engineering external interships – Dynamic Gravity Systems, LLC, Denver, Colorado, United States (June 2024 – August 2024)


    Technical skills

    • Programming languages: Python, Java, C++, JavaScript, Go, MATLAB

    • Libraries: Pandas, NumPy, TensorFlow, Keras, Scikit-Learn

    • Projects development: Spring Boot, Spring MVC, Maven

    • Databases and Cloud Computing: AWS, Amazon S3, Oracle, BigQuery, PostgreSQL, PL/SQL

    • Version control and DevOps: GitHub, GitLab.

    • Artificial Intelligence: Neural Networks, Super Resolution, AI, Machine Learning, GAN, Stable Diffusion

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