2 research outputs found
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
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.Comment: 32 pages, 17 figures, 5 tables, preprint submitted to Neurocomputin
Real-World Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR), which aims to reconstruct a
high-resolution (HR) image from a low-resolution (LR) observation, has been an
active research topic in the area of image processing in recent decades.
Particularly, deep learning-based super-resolution (SR) approaches have drawn
much attention and have greatly improved the reconstruction performance on
synthetic data. Recent studies show that simulation results on synthetic data
usually overestimate the capacity to super-resolve real-world images. In this
context, more and more researchers devote themselves to develop SR approaches
for realistic images. This article aims to make a comprehensive review on
real-world single image super-resolution (RSISR). More specifically, this
review covers the critical publically available datasets and assessment metrics
for RSISR, and four major categories of RSISR methods, namely the degradation
modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR,
and self-learning-based RSISR. Comparisons are also made among representative
RSISR methods on benchmark datasets, in terms of both reconstruction quality
and computational efficiency. Besides, we discuss challenges and promising
research topics on RSISR.Comment: 18 pages, 12 figure, 4 table