Clearview: An Image Enhancement and Restoration

Abstract

Digital images captured in real-world environments often suffer from quality degradation caused by low illumination, excessive brightness, blur, and noise. Such degradations reduce the visual clarity of images and limit their usability in applications such as photography, surveillance, documentation, and media processing. Conventional image editing tools require manual parameter adjustments such as brightness, contrast, and sharpness correction, which makes the enhancement process time-consuming and highly dependent on user expertise. This project proposes Clear-View, an automated image restoration and enhancement system designed to improve the quality of degraded images using a combination of deep learning models and traditional image-processing techniques. The system automatically analyzes the condition of the input image and identifies degradation types such as low-light conditions, overexposure, noise, or blur. Based on this analysis, the system selects an appropriate enhancement model, including Zero-DCE for low-light enhancement and Pix2PixHD for exposure correction, along with image-processing techniques such as CLAHE, denoising, and sharpening. Index Terms: The system operates through a structured pipeline consisting of preprocessing, degradation detection, enhancement, and restoration stages. Experimental evaluation shows that the proposed system significantly improves image clarity, contrast, and brightness while minimizing manual effort. The ClearView system provides an efficient and user-friendly platform for automated image restoration and enhancement.

Introduction

The text introduces ClearView, an intelligent image enhancement and restoration framework designed to improve degraded images affected by noise, blur, poor lighting, and exposure issues commonly found in real-world visual data.

Traditional image enhancement methods (like histogram equalization and filtering) are limited because they rely on fixed rules and often fail when multiple distortions occur simultaneously. Even modern deep learning models (such as CNNs, GANs, and transformer-based methods) are usually task-specific, computationally heavy, and lack a unified system for handling different types of degradation adaptively.

To overcome these limitations, ClearView proposes an adaptive, automated, and hybrid approach that combines deep learning with classical image-processing techniques. The system first analyzes the input image to detect degradation types, then selects suitable enhancement methods dynamically. It integrates preprocessing, degradation detection, enhancement, and output evaluation into a single pipeline.

The framework uses convolutional neural networks for feature extraction and image restoration, along with traditional techniques like denoising and contrast adjustment to improve visual quality. Performance is evaluated using metrics such as PSNR and SSIM to ensure structural accuracy and clarity.

The methodology involves a structured pipeline: image acquisition, preprocessing, degradation analysis, adaptive enhancement, and quality evaluation. It is implemented using Python with tools like TensorFlow, PyTorch, and OpenCV, trained on paired datasets of degraded and high-quality images.

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Copyright

Copyright © 2026 Dr. Hemavathi C Purad, Syed Abu Abdulla, U. Sri Lakhmi, Uday Kumar G, Vaibhav K. 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.