Image_denoising-24

This project aims to develop a deep-learning model for image denoising. The model utilizes a Convolutional Neural Network (CNN) architecture to learn and perform the denoising task. The project involves data preprocessing, model training, evaluation, and application of the trained model to noisy images. #Objectives

• Develop a CNN-based model for image denoising.
• Train the model on a dataset containing pairs of noisy and clean images.
• Evaluate the performance of the model using various metrics.
• Apply the trained model to real-world noisy images to test its effectiveness.

#Dataset The dataset used for this project contains pairs of images, each consisting of a noisy image and its corresponding clean version. The noisy images are generated by adding artificial noise to the clean images. The dataset is divided into training, validation, and test sets to facilitate model training and evaluation.

#Methodology

1. Data Preprocessing: Load the dataset and split it into training, validation, and test sets. Apply transformations such as resizing, normalization, and data augmentation to the images to enhance the model's robustness.

2. Model Architecture: Design a Convolutional Neural Network (CNN) for image denoising. The CNN consists of several layers, including convolutional layers, activation functions (e.g., ReLU), and normalization layers.

3. Training: Define the loss function (e.g., Mean Squared Error) and optimizer (e.g., Adam). Train the model using the training dataset, adjusting the model's parameters to minimize the loss function. Use the validation dataset to tune hyperparameters and avoid overfitting.

4. Model Loading and Saving: Save the trained model parameters to a file for later use. Load the model parameters from the file when needed using the line:

5. Evaluation: Evaluate the model's performance on the test dataset using metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). Compare the denoised images with the original clean images to assess the visual quality.

6. Inference: Apply the trained model to new noisy images. Visualize and analyze the denoised images to demonstrate the model's effectiveness.

#Results

• The model's performance is measured in terms of PSNR and SSIM, showing significant improvement in image quality after denoising.
• Visual comparisons between noisy, denoised, and clean images illustrate the model's ability to remove noise while preserving important image details.