  In recent years, much research has been conducted on image super-resolution
(SR). To the best of our knowledge, however, few SR methods were concerned with
compressed images. The SR of compressed images is a challenging task due to the
complicated compression artifacts, while many images suffer from them in
practice. The intuitive solution for this difficult task is to decouple it into
two sequential but independent subproblems, i.e., compression artifacts
reduction (CAR) and SR. Nevertheless, some useful details may be removed in CAR
stage, which is contrary to the goal of SR and makes the SR stage more
challenging. In this paper, an end-to-end trainable deep convolutional neural
network is designed to perform SR on compressed images (CISRDCNN), which
reduces compression artifacts and improves image resolution jointly.
Experiments on compressed images produced by JPEG (we take the JPEG as an
example in this paper) demonstrate that the proposed CISRDCNN yields
state-of-the-art SR performance on commonly used test images and imagesets. The
results of CISRDCNN on real low quality web images are also very impressive,
with obvious quality enhancement. Further, we explore the application of the
proposed SR method in low bit-rate image coding, leading to better
rate-distortion performance than JPEG.
