Bidirectional image denoising with blurred image feature

Image denoising remains a classical yet still challenging problem, because the noise can destroy details and cause severe information loss. In recent years, various well-designed CNN-based methods have been extensively applied in image denoising because of the strong learning ability. However, most of them share an unidirectional procedure, which directly learns a mapping from noisy input to a clean image without focusing on the over-smoothed state of the denoising process, limiting the richness of extracted features. Different from previous works, we propose a blurred image feature guided CNN (BFCNN) network that implements a novel blurring-adjusting strategy to address the complex denoising problem via two stages. In stage 1, we build a blurring module (BM) to capture over-smoothed features from noisy observations and generate the blurred image restoration, which is a less informative version of the clean image. Furthermore, a multilevel concatenating module (CM) and an adjusting module (AM) are then designed to recover more detailed information in stage 2. These two modules are jointly designed to restore a properly-smoothed image from the over-smoothed blurred image and the given under-smoothed noisy image. Comparing to the traditional denoising process, the proposed blurring-adjusting strategy produces a precise denoised image more efficiently by converting the unidirectional denoising process into a bidirectional denoising process. To our knowledge, this is the first study that utilizes the over-smoothed image to address the denoising problem. Extensive experiments demonstrate the superiority of our BFCNN with more accurate reconstruction quality and achieve competitive quantitative results among current CNN-based methods. This research will release the code upon acceptance.