SSIM index

Image denoising is a crucial topic in image processing. Noisy images are generated due to technical and environmental errors. Therefore, it is reasonable to consider image denoising an important topic to study, as it also helps to resolve other image processing issues. However, the challenge is that the classical techniques used are time-consuming and not flexible enough. This article compares two major neural network architectures which look promising to resolve these issues. The AutoEncoder and UNET is now the most researched subject in deep learning for image denoising. Multiple model architectures are designed, implement, and evaluated. The dataset is preprocessed and then it is used to train and test the model. It is clearly shown in this paper which model performs the best in this task by comparing both models using the most used parameters to evaluate image quality PSNR and SSIM.

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.

Image denoising is an important aspect of image processing. Noisy images are produced as a result of technical and environmental flaws. As a result, it is reasonable to consider image denoising an important topic to research, as it also aids in the resolution of other image processing issues. The challenge, however, is that the traditional techniques used are time-consuming and inflexible. This article purposed a system of classifying and denoising noised images. A CNN and UNET based model architecture is designed, implement, and evaluated. The facial image dataset is processed and then it is used to train, valid and test the models. During preprocessing, the images are resized into 48*48, normalize, and various noises are added to the image. The preprocessing for each model is a bit different. The training and validation accuracy for the CNN model is 99.87% and 99.92% respectively. The UNET model is also able to get optimal PSNR and SSIM values for different noises.