A Novel Approach for Identifying and Eliminating the Degradations in Real Time Images

Satellite imagery plays a crucial role in various applications, but the quality of the images can be degraded by noise. Accurate noise detection is essential for effective image enhancement and analysis. This project proposes a hybrid approach for noise detection and enhancement in satellite images by integrating deep learning and conventional techniques. We use a convolutional neural network (CNN) model along with Daubechies wavelet coefficient and statistically extracted coefficient paired with feature components extracted after applying median and high-pass filter for noise detection, to classify the type of noise in satellite images. The CNN model is trained on a dataset of images with various noises, learning to discriminate between different noise patterns. After classifying the type of noise, we apply a enhancement approach utilizes deep learning techniques by leveraging U-Net based CNN model to reduce noise levels in an image. Our approach achieves a validation accuracy of 91.29% for noise classification and a PSNR of 37.78 dB for image enhancement, outperforming several state-of-the-art methods.