Wavelet Based Image Restoration via Multi-Scale Decomposition

Image denoising is one of the fundamental task in digital image processing, aimed at enhancing the visual quality of images by removing unwanted noise. Image quality can be compromised by different noise sources, including Gaussian, salt-and-pepper, and speckle noise. This paper proposes a hybrid image denoising approach that integrates convolutional neural networks (CNNs) and wavelet transform. The proposed approach first applies a CNN to the noisy image to remove high-level noise, such as textures and patterns. After denoising, the image undergoes decomposition through the wavelet transform, breaking it into various frequency sub-bands. The wavelet coefficients in the high-frequency subbands are then thresholded to remove low-level noise, such as Gaussian noise. Finally, the image is reconstructed using the inverse wavelet transform. The proposed approach has several advantages over traditional image denoising methods. It is able to remove both high-level and low-level noise effectively. It is more robust to different types of noise and able to preserve fine image details.