Edge‐aware image filtering using a structure‐guided CNN
Si-Jung Kim, Changho Song, Jinbeum Jang, Joonki Paik · IET Image Processing · 2019
Image filtering is a fundamental preprocessing step for accurate, robust computer vision applications such as image segmentation, object classification, and reconstruction. However, many convolutional neural network (CNN)‐based methods tend to lose significant edge information in the output layer, and generate undesired artefacts in the feature extraction layers. This study presents a deep CNN model for edge‐aware image filtering. The proposed network model consists of three sub‐networks: (i) feature extraction, (ii) convolution artefact removal, and (iii) structure extraction networks. The proposed network model has an end‐to‐end trainable architecture that does not need any post‐processing steps. Especially, the structure extraction network can successfully preserve significant edges. The proposed filter outperforms state‐of‐the‐art denoising filters in terms of both objective and subjective measures, and can be used for various image enhancement and restoration problems such as edge‐preserving smoothing, image denoising, deblurring, and deblocking.