An Efficient on Scale and Structure Aware Image Smoothening for Watermarked Images with Denoising in Improved DCNN– A Review

Abstract

This research explores novel approaches to image smoothening through the integration of Deep Convolutional Neural Networks (CNN) and Interactive Segmentation techniques. The objective is to enhance image quality by reducing noise and artifacts while preserving important details. We propose a deep learning model that leverages the power of CNNs to automatically learn and adapt image features for effective smoothening. Additionally, interactive segmentation is incorporated to involve user input, allowing for personalized control over the smoothening process. Experimental results demonstrate significant improvements in image quality metrics, indicating the effectiveness of the proposed method. The combination of deep learning and interactive segmentation offers a promising solution for image enhancement, with potential applications in various domains such as medical imaging, computer vision, and photography.