Generative Adversarial Network Powered Image Transformation Enhancing, Completing and Converting Visuals

Abstract

This study addresses conventional image processing shortcomings via one cohesive system integrating SRGAN, DeepFillv2, and CycleGAN (Generative Adversarial Networks). Image enhancement along with clever completion also versatile style transformations including colorization and decolorization are accessible through an intuitive Streamlit web interface. SRGAN is known to excel at improving image resolution and DeepFillv2 reconstructs damaged areas skillfully with full contextual awareness. CycleGAN eases style changes between domains without need of matched datasets. The system performs during real-time, features dependability, and outputs superior visuals. SSIM evaluation validates its utility. It makes this an important instrument for artists, designers, and photographers.

Introduction

1. Overview

This project leverages Generative Adversarial Networks (GANs) to enhance, complete, and convert images. GANs have revolutionized image processing with their ability to generate high-quality visuals. This study focuses on three advanced models:

• SRGAN – for super-resolution and image enhancement.

• DeepFillv2 – for intelligent image inpainting (completion).

• CycleGAN – for unpaired image conversion (e.g., colorization, style transfer).

2. Literature Survey

Several foundational works inform this project:

• Isola et al. (2017): Proposed conditional GANs for image-to-image translation with paired data.

• CycleGAN (Park & Isola, 2017): Enabled domain translation without paired examples using cycle-consistency loss.

• DCGAN (Mehralian & Karasfi, 2018): Used for unsupervised learning and improving resolution.

• SRGAN with Wavelet Transform (Dhanasekaran & Raghu, 2022): Improved MRI image quality, showing high practical value in medical imaging.

3. Objective

To create a user-friendly web-based tool (using Streamlit) that:

• Performs three major image manipulation tasks using GANs.

• Ensures high realism, accuracy, and visual quality.

• Is accessible to professionals in digital art, design, and photography.

4. System Analysis

A. Limitations of Existing Systems

• DCGAN and Pix2Pix struggle with blurry textures, long training times, poor inpainting in large gaps, and inaccurate colors.

B. Proposed System Improvements

• SRGAN for sharper, high-resolution output.

• DeepFillv2 for context-aware, precise inpainting.

• CycleGAN for realistic, flexible domain transformations without paired data.

5. Methodology and Architecture

System Layers:

• User Layer: Users upload images via a Streamlit interface.

• Processing Layer: Routes tasks to the appropriate GAN model.

• Storage Layer: Displays and stores generated images.

Process Steps:

1. Image Upload: User selects image and task.

2. Preprocessing: Resize, normalize, convert color channels.

3. Model Selection: Based on the task (SRGAN, DeepFillv2, CycleGAN).

4. Image Generation: Model performs enhancement, completion, or conversion.

5. Storage & Output: Results are displayed and downloadable.

6. Evaluation: Uses SSIM when ground truth is available for quality assessment.

6. Results & Performance Analysis

• Demonstrated significant improvements across all tasks.

• Visual output shows enhanced sharpness, realistic inpainting, and accurate style conversions (colorization and decolorization).

Conclusion

This project effectively demonstrates a deep learning system for image transformation, powered by Generative Adversarial Networks (GANs), capable of performing image enhancement, completion, and conversion within a unified platform. By leveraging SRGAN, DeepFillv2, and CycleGAN architectures, the system provides high-quality, realistic visual outputs across a variety of image transformation tasks. The Streamlit-based interface ensures accessibility for users with varying technical expertise, allowing them to interact with the system easily and effectively. Overall, this approach simplifies complex image editing processes and has strong potential in domains such as digital art, media restoration, and automated visual content generation. There are several opportunities to enhance this GAN-powered image transformation system in future versions. The platform can be extended to include additional functionalities such as noise removal, object manipulation, and background editing to increase its versatility. Incorporating real-time input from devices like cameras or drawing tablets could expand its use in creative and professional workflows. The integration of more advanced GAN variants or hybrid models could further improve output quality and model robustness. Additionally, deploying the system on cloud platforms would allow broader access, support real-time processing at scale, and make it feasible for deployment in industries such as entertainment, design, and e-commerce.

References

[1] Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5967–5976. https://doi.org/10.48550/arXiv.1611.07004 [2] Park, T., Zhu, J.-Y., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2223–2232. https://doi.org/10.48550/arXiv.1703.10593 [3] Mehralian, M., & Karasfi, B. (2018). Unsupervised Representation Learning with Deep Convolutional GANs (DCGAN). 2018 9th International Conference on Computer and Knowledge Engineering (ICCKE), 147–152. https://doi.org/10.1109/ICCKE.2018.8576265 [4] Dhanasekaran, H., & Raghu, B. (2022). MRI Super-Resolution using Generative Adversarial Network and Discrete Wavelet Transform. International Journal of Advanced Research in Computer and Communication Engineering, 11(1), 1–6. https://doi.org/10.48550/rg.367196575

Copyright

Copyright © 2025 Vaddepelli Sathwika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.