Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{194311,
        author = {Nandyala Jyothirmayi Reddy and Mukkanti Deekshitha and Akuthota Ramprasad and Sake Saikumar and Shabeer V},
        title = {NEUROARTIFY:AN AI-BASED WEB PLATFORM FOR ARTISTIC IMAGE TRANSFORMATION AND ENHANCEMENT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3507-3516},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194311},
        abstract = {In the contemporary digital landscape, the intersection of computer vision and creative expression has birthed a new era of automated artistry. This paper presents NeuroArtify, a unified, high-performance web ecosystem designed to democratize professional-grade image manipulation. Unlike fragmented tools that specialize in isolated tasks, NeuroArtify integrates four state-of-the-art neural pipelines: a hybrid Vision Transformer (ViT) and VGG-19 architecture for Artistic Style Transfer, U²-Net for high-precision background removal, Enhanced Super-Resolution GAN (ESRGAN) for perceptual upscaling, and a CRAFT-based inpainting module for semantic text elimination. By utilizing a ReactJS-driven frontend and a high-concurrency Flask backend, the platform ensures seamless asynchronous processing. Quantitative evaluations demonstrate competitive performance, yielding a Peak Signal-to-Noise Ratio (PSNR) of 26.7 dB for image enhancement and a Structural Similarity Index (SSIM) of 0.82 for text removal. Our findings confirm that the integration of global attention mechanisms with local spatial filters provides a superior balance between stylistic innovation and structural fidelity.},
        keywords = {Neural Style Transfer (NST), ESRGAN, Vision Transformers (ViT), Image Enhancement, Computer Vision, U²-Net, Generative Adversarial Networks (GAN).},
        month = {March},
        }