Study on Noise Suppressed Image Enhancing Environment (NSIEE)
Year: 2025
Type: article
Abstract: Image enhancement plays a crucial role in improving the visual quality of images for various applications, including photography, medical imaging, and security. Enhancing an image by increasing its contrast is one effective way of image enhancement, but real-world challenges such as noise, blurriness, and loss of details require more advanced techniques. The Noise Suppressed Image Enhancing Environment (NSIEE) integrates both traditional and deep learning-based methods to enhance images efficiently. Traditional techniques like CLAHE, adaptive sharpening, and Non-Local Means denoising improve contrast and reduce noise, while deep learning-based approaches enable intelligent feature extraction, resolution upscaling, and color preservation. The system provides a real-time side-by-side comparison, computes quality metrics like PSNR and SSIM, and features a Flask-based backend with OpenCV and TensorFlow for seamless image processing. With an intuitive web interface and scalable architecture, NSIEE offers a powerful solution for high-quality image enhancement.
Authors: Chim, Shruti Rajendra, Raut, Maithili Rajesh, Akotkar, Shreya Nandkumar, Kalore, Komal Ramesh, Telang, Maithili Murlidhar, Deshmukh, Dr. Anand. B.
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6893231579
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