5 research outputs found
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
Several dual-domain convolutional neural network-based methods show
outstanding performance in reducing image compression artifacts. However, they
suffer from handling color images because the compression processes for
gray-scale and color images are completely different. Moreover, these methods
train a specific model for each compression quality and require multiple models
to achieve different compression qualities. To address these problems, we
proposed an implicit dual-domain convolutional network (IDCN) with the pixel
position labeling map and the quantization tables as inputs. Specifically, we
proposed an extractor-corrector framework-based dual-domain correction unit
(DCU) as the basic component to formulate the IDCN. A dense block was
introduced to improve the performance of extractor in DRU. The implicit
dual-domain translation allows the IDCN to handle color images with the
discrete cosine transform (DCT)-domain priors. A flexible version of IDCN
(IDCN-f) was developed to handle a wide range of compression qualities.
Experiments for both objective and subjective evaluations on benchmark datasets
show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits
excellent abilities to handle a wide range of compression qualities with little
performance sacrifice and demonstrates great potential for practical
applications.Comment: accepted by IEEE Transactions on Circuits and Systems for Video
Technology(T-CSVT
Concatenated Attention Neural Network for Image Restoration
In this paper, we present a general framework for low-level vision tasks
including image compression artifacts reduction and image denoising. Under this
framework, a novel concatenated attention neural network (CANet) is
specifically designed for image restoration. The main contributions of this
paper are as follows: First, by applying concise but effective concatenation
and feature selection mechanism, we establish a novel connection mechanism
which connect different modules in the modules stacking network. Second, both
pixel-wise and channel-wise attention mechanisms are used in each module
convolution layer, which promotes further extraction of more essential
information in images. Lastly, we demonstrate that CANet achieves better
results than previous state-of-the-art approaches with sufficient experiments
in compression artifacts removing and image denoising
AIM 2019 Challenge on Image Demoireing: Methods and Results
This paper reviews the first-ever image demoireing challenge that was part of
the Advances in Image Manipulation (AIM) workshop, held in conjunction with
ICCV 2019. This paper describes the challenge, and focuses on the proposed
solutions and their results. Demoireing is a difficult task of removing moire
patterns from an image to reveal an underlying clean image. A new dataset,
called LCDMoire was created for this challenge, and consists of 10,200
synthetically generated image pairs (moire and clean ground truth). The
challenge was divided into 2 tracks. Track 1 targeted fidelity, measuring the
ability of demoire methods to obtain a moire-free image compared with the
ground truth, while Track 2 examined the perceptual quality of demoire methods.
The tracks had 60 and 39 registered participants, respectively. A total of
eight teams competed in the final testing phase. The entries span the current
the state-of-the-art in the image demoireing problem.Comment: arXiv admin note: text overlap with arXiv:1911.0249
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
Lossy compression brings artifacts into the compressed image and degrades the
visual quality. In recent years, many compression artifacts removal methods
based on convolutional neural network (CNN) have been developed with great
success. However, these methods usually train a model based on one specific
value or a small range of quality factors. Obviously, if the test image's
quality factor does not match to the assumed value range, then degraded
performance will be resulted. With this motivation and further consideration of
practical usage, a highly robust compression artifacts removal network is
proposed in this paper. Our proposed network is a single model approach that
can be trained for handling a wide range of quality factors while consistently
delivering superior or comparable image artifacts removal performance. To
demonstrate, we focus on the JPEG compression with quality factors, ranging
from 1 to 60. Note that a turnkey success of our proposed network lies in the
novel utilization of the quantization tables as part of the training data.
Furthermore, it has two branches in parallel---i.e., the restoration branch and
the global branch. The former effectively removes the local artifacts, such as
ringing artifacts removal. On the other hand, the latter extracts the global
features of the entire image that provides highly instrumental image quality
improvement, especially effective on dealing with the global artifacts, such as
blocking, color shifting. Extensive experimental results performed on color and
grayscale images have clearly demonstrated the effectiveness and efficacy of
our proposed single-model approach on the removal of compression artifacts from
the decoded image.Comment: Accepted for publication in the IEEE Transactions on Image Processin
Quantization Guided JPEG Artifact Correction
The JPEG image compression algorithm is the most popular method of image
compression because of its ability for large compression ratios. However, to
achieve such high compression, information is lost. For aggressive quantization
settings, this leads to a noticeable reduction in image quality. Artifact
correction has been studied in the context of deep neural networks for some
time, but the current state-of-the-art methods require a different model to be
trained for each quality setting, greatly limiting their practical application.
We solve this problem by creating a novel architecture which is parameterized
by the JPEG files quantization matrix. This allows our single model to achieve
state-of-the-art performance over models trained for specific quality settings.Comment: Published in the proceedings of ECCV 2020, please see our released
code and models at https://gitlab.com/Queuecumber/quantization-guided-a