Frequency-Domain Deep Guided Image Denoising

Despite the tremendous advances in denoising techniques, it's still challenging to restore a clean image with salient structures based on one noisy observation, especially at high noise levels. In this work, we propose a frequency-domain guided denoising algorithm to conduct denoising with the help of a well-aligned guidance image. Thanks to their structural correlations, the frequency characteristics of the guidance image can indicate whether the frequency coefficients of the noisy target image are contributed by noise or textures. Therefore, the explicit frequency decomposition enables our denoising model to avoid over-smoothing detailed contents. However, as two input images are usually captured in different fields, their structures are not always consistent. Therefore, we model guided denoising with an optimization problem which considers both the representation model of the guidance image and the fidelity to the noisy target. Further, we design a convolutional neural network, called as FGDNet, to explore the optimal solution. Due to the visual masking phenomenon, human eyes are sensitive to noise in the flat areas, but may not perceive noise around edges or textures. Therefore, we expect to remove as much noise as possible to guarantee the spatial smoothness of flat contents, while also preserving high-frequency structures. Through frequency decomposition, our model can process the low-frequency and high-frequency contents separately. We also adopt a frequency-relevant loss function to train the network. Experimental results show that, compared with state-of-the-art guided and non-guided denoisers, our FGDNet achieves higher denoising accuracy and better visual quality in both flat and texture-rich regions.