Deep Region Adaptive Denoising for Texture Enhancement

Image denoising is a highly challenging problem yet important task in image processing. Recently, many CNN-based denoising methods have made great performances but they commonly denoise blindly texture and non-texture regions together. This frequently leads to excessive texture smoothing and detail loss. To address this issue, we propose a novel region adaptive denoising network that adjusts the denoising strength according to region textureness. The proposed network conducts denoising tasks for texture and non-texture areas independently to improve the visual quality of the resulting image. To this end, we first generate a texture map that separates the image into texture and non-texture region. Because the difference between texture and non-texture is more evident in the frequency domain than in the spatial domain, the classification is performed through discrete cosine transform (DCT). Second, guided by the texture map, denoising is performed independently in two subnets, corresponding to the texture and non-texture regions. This allows the texture subnet to avoid excessive smoothing of high frequency details, and the non-texture subnet to maximize noise reduction in flat regions. Finally, a cross fusion that takes into intra- and inter-relationship between two resulting features is proposed. The cross fusion highlights the discriminant features from two subnets without degradation when combining the output of two subnets, and thus helps enhancing the performance of regions adaptive denoising. The superiority of the proposed method is validated on both synthetic and real-world images. We demonstrate that our method outperforms the existing methods in both objective scores and subjective image quality, in particular showing outstanding results in the restoration of visually sensitive textures. Furthermore, ablation study shows that our network can adaptively control the noise removal strength by manually manipulating the texture map and that the details of the texture region can be further improved. This also can simplify the cumbersome noise tuning process when deploying deep neural networks (DNN) architectures into products.