Enhanced Adaptive Wiener Filtering for Frequency-Varying Noise with Convolutional Neural Network-Based Feature Extraction

Denoising has long been a challenge in image processing. Noise appears in various forms, such as additive white Gaussian noise (AWGN) and Poisson noise across different frequencies. This study aims to denoise images without prior knowledge of the noise distribution. First, we estimate the noise power in the frequency domain to approximate the local signal-to-noise ratio (SNR) and guide an adaptive Wiener filter. The initial denoised result is obtained by assembling the locally filtered patches. However, since the Wiener filter is a low-pass filter, it can remove fine details along with the noise. To overcome this limitation, we post-process the noise and interpolate it between the denoised and original noisy patches to enhance the denoised image. We also mask the frequency domain to avoid grid-like artifacts. Additionally, we introduce a convolutional neural network-based refinement technique to the spatial domain to recover latent textures lost during denoising. The method presents the effectiveness of masking and feature extraction.