One tough problem of image inpainting is to restore complex structures in the corrupted regions. It motivates interactive image inpainting which leverages additional hints, e.g., sketches, to assist the inpainting process. A sketch is simple and intuitive for end users to provide, but meanwhile has free forms with much randomness. Such randomness may confuse the inpainting models, and incur severe artifacts in completed images. To better facilitate image inpainting with sketch guidance, we propose a two-stage image inpainting system, termed SketchRefiner. The first stage of our approach serves as a data provider that simulates real sketches and derives the capability of sketch calibration from the simulated data. In the second stage, our approach aligns the sketch guidance with the inpainting process so as to elevate image inpainting with sketches. We also propose a real-world test protocol to address the evaluation of inpainting methods upon practical applications with user sketches. Experimental results on three prevailing benchmark datasets, i.e., CelebA-HQ, Places2, and ImageNet, and the proposed test protocol demonstrate the state-of-the-art performance of our approach, and its great potentials upon real-world applications. Further analyses illustrate that our approach effectively utilizes sketch information as guidance and eliminates the artifacts due to the free-form sketches.

The capability of image semantic segmentation may be deteriorated due to the noisy input image, where image denoising prior to segmentation may help. Both image denoising and semantic segmentation have been developed significantly with the advance of deep learning. In this work, we are interested in the synergy between these two tasks by using a holistic deep model. We observe that not only denoising helps combat the drop of segmentation accuracy due to the noisy input, but also pixel-wise semantic information boosts the capability of denoising. We then propose a boosting network to perform denoising and segmentation alternately. The proposed network is composed of multiple segmentation and denoising blocks (SDBs), each of which estimates a semantic map and then uses the map to regularize denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that on clean images, and segmentation and denoising are both boosted as the number of SDBs increases. On the Cityscapes dataset, using three SDBs improves the denoising quality to 34.42 dB in PSNR, and the segmentation accuracy to 66.5 in mIoU, when the additive white Gaussian noise level is 50.