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OptiVim-o : Optical Visual Modulator and Organizer

🌐 Access the platform here: https://optivimo.online/

Group Information

University: TOBB University of Economics and Technology

Team Name: FiveHorsemen

Team Members

  • Korhan Sevinç - AI Researcher, Student & Team Lead
  • Utku Murat Atasoy - AI Researcher, Student
  • Kerem Ay - AI Researcher, Student
  • Umut Özdemir - AI Researcher, Student
  • Süleyman Emir Akın - AI Researcher, Student

About Us

We are a team of passionate senior computer science students dedicated to improving the usage of image enhancement experience. Our diverse backgrounds in software development, design, AI, and user experience help us create innovative solutions for real-world problems.

Project Description

In the digital age, images have become central to communication, documentation, and creative expression. However, existing image editing tools often fall short in meeting the diverse needs of users. Many platforms either offer a limited range of features or require significant technical expertise.

OptiVim-o (Optical Visual Modulator and Organizer) is an all-in-one, AI-powered image enhancement platform that bridges this gap by combining deep learning and modern computer vision techniques with an intuitive interface. The platform supports a variety of advanced features, designed to cater to both professional and casual users.

Core Features

  1. Image Resize
    Enabling users to resize images to custom dimensions or reduce file size intelligently without quality loss—using interpolation techniques and smart resizing algorithms.

  2. Format Conversion
    Support for image format transformations across JPEG, PNG, BMP, and other common types, improving compatibility and storage optimization.

  3. Image Rotation
    Allows fine-grained rotation by any degree to correct orientation or apply creative effects, with support for anti-aliasing and bilinear interpolation.

  4. Colorization
    Revives grayscale or black-and-white images through deep learning-based automatic colorization, offering realistic tones and historically plausible palettes.

  5. Image Summarization
    Uses AI to extract semantic content from an image and summarize it in natural language—combining computer vision and NLP techniques.

  6. Noise Removal
    Utilizes deep denoising networks to remove various types of noise (Gaussian, salt-and-pepper, etc.) while preserving details in low-quality or compressed images.

  7. Image Restoration
    Repairs old or damaged images through intelligent inpainting, removing scratches, stains, and aging artifacts to recover original aesthetics.

  8. Image Super-Resolution
    Enhances low-resolution images through AI-powered upscaling techniques like SRCNN or ESRGAN to improve clarity and detail.

  9. Background Removal
    Leverages semantic segmentation to separate foreground subjects with high accuracy, enabling creative reusability of visual elements.

  10. Age Manipulation
    Applies age progression or regression effects using GAN-based facial transformation models for natural and photorealistic edits.

Additional Features

  1. Generative Fill (Outpainting)
    Extends image borders by filling in missing or expanded areas using diffusion models. Ideal for artwork expansion and creative extensions.

  2. Object Removal
    Detects and removes unwanted objects or individuals using inpainting techniques to intelligently reconstruct the background.

  3. Object Recolor
    Allows users to recolor specific objects like clothing or accessories while preserving shading, lighting, and texture.

  4. Cartoon Stylization
    Converts images into cartoon-like illustrations using neural style transfer. Perfect for avatars and creative visuals.

  5. Pixel Art Conversion
    Transforms standard images into retro-style pixel art. Great for game developers and digital artists.

  6. Pixel Art Background and Character Generation
    Automatically generates pixel art assets from regular images—ideal for games, animations, and storytelling media.

  7. Contrast Enhancement
    Improves contrast and brightness using histogram equalization and adaptive machine-learned filters based on content.


Models, Folders and Files

For additional info, sprint logs and pages , whole project code in a zip file (including models), logo's, and final project report can be found at : https://drive.google.com/drive/folders/12Td4a5IuKAMbDdW-hPabQqFt4NuL2vW4?usp=drive_link

Contact

For any additional information or inquiries, please contact us via email: ksevinc@etu.edu.tr

Acknowledgements

  • Special thanks to Mehmet Efe Ergin (@K4hveci) for the awesome logo design!

Citation

If you use this project or find it useful, please cite it as follows:

Korhan Sevinc, Utku Murat Atasoy, Kerem Ay , Umut Özdemir, Süleyman Emir Akın   , GitHub repository, 2025.
Available at: https://github.com/korhansevinc/OptiVim-o.git

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