  Due to the limits of bandwidth and storage space, digital images are usually
down-scaled and compressed when transmitted over networks, resulting in loss of
details and jarring artifacts that can lower the performance of high-level
visual tasks. In this paper, we aim to generate an artifact-free
high-resolution image from a low-resolution one compressed with an arbitrary
quality factor by exploring joint compression artifacts reduction (CAR) and
super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR
neural network (CAJNN) that integrates both local and non-local features to
solve CAR and SR in one-stage. Finally, a deep reconstruction network is
adopted to predict high quality and high-resolution images. Evaluation on CAR
and SR benchmark datasets shows that our CAJNN model outperforms previous
methods and also takes 26.2% shorter runtime. Based on this model, we explore
addressing two critical challenges in high-level computer vision: optical
character recognition of low-resolution texts, and extremely tiny face
detection. We demonstrate that CAJNN can serve as an effective image
preprocessing method and improve the accuracy for real-scene text recognition
(from 85.30% to 85.75%) and the average precision for tiny face detection (from
0.317 to 0.611).
