Learning From Synthetic Data For Crowd instance Segmentation in The Wild

Since information loss incurred in lossy image compression is irreversible, it is extremely challenging to achieve a satisfactory effect of artifacts removal by using the compressed image itself only. To solve the problem, a novel reference-based artifacts removal network (RARN) is proposed in this paper, which exploits a high-quality reference image to provide useful information for facilitating the removal of artifacts and the reconstruction of details. in our RARN, a feature extraction module is first established, which takes both the compressed and reference images as inputs and produces multi-scale feature pairs as outputs. Then, a feature transfer non-local block (FTNB) is developed to match the feature pairs and transfer relevant features from the reference image to the compressed one in the feature space. Finally, image information is recovered from the multi-scale outputs of FTNB by using a reconstruction module. Extensive experimental results clearly show that our proposed RARN can deliver superior performance over a number of state-of-the-art methods.