[visionlist] CFP: Special Issue on Deep-Learning Based Image Enhancement and Compression @ Frontiers in Signal Processing

Wenhan Yang ywhlist150578209 at gmail.com
Sun Oct 3 04:17:25 -04 2021



*Call for Papers: Special Issue on Deep-Learning Based Image
Enhancement and Compression *

*                 @ Frontiers in Signal Processing*

Website: https://www.frontiersin.org/research-topics/23539/deep-learning-based-image-enhancement-and-compression


Image/video quality assessment, enhancement, and compression are
fundamental topics in the low-level computer visions which have
witnessed rapid progress in the last two decades. Due to various
degradations in the image and video capturing, transmission, and
storage, image and video might incur a series of undesirable effects,
such as low resolution, low light condition, rain streak, blackness,
raindrop occlusions, and high-frequency detail loss, etc. The
estimation and recovery of these degradations are highly ill-posed.
With the wealth of statistic-based frameworks, i.e. traditional
Maximum-a-Posteriori (MAP) Estimation and Rate-distortion joint
Optimization (RDO), and learning-based tools, e.g. deep networks,
meta-learning, and adversarial learning, many recent
deep-learning-based methods have shown their significant performance
gains over traditional non-deep methods.

Existing image/video quality assessment, restoration and compression
methods have remaining issues to be addressed. The challenges arise
from the following aspects:
1) As enhancement/compression models are trained on the training data
collected from limited scenes or occasionally synthetically generated
ones, their performances might sharply degrade on real-world images
and videos when there are domain gaps between real applications and
training data.
2) Existing losses used for the model training are proven to be
misaligned with the human vision experiences, more efforts are
expected to design better measures to describe human vision
experiences closely.
3) Existing methods are mainly designed for human vision. With the big
data captured from smart cities and the Internet of Things, more
applications expect to feed the data into machines. It would be the
new critical issue to build new approaches to enhance and compress
images/videos for both humans and machines.
4) Existing models include more than millions of parameters, which
pose obstacles to real applications.

Topics of interest include (but are not limited to):

   - Novel architectures, models, and approaches for image and video
quality assessment, restoration and compression.
   - Novel theories, optimization methods, training skills for
training models and networks for low-level vision.
   - Computationally efficient networks for image/video quality
assessment, restoration and compression.
   - Learned enhancement and compression models for humans and machines.
   - Deep learning-based techniques that improve the performance of
existing codecs and standards.
   - Quality assessment methods that are wellf aligned to human visual
   - New enhancement/compression methods guided by perceptual measures
or analysis tasks.
   - Explainable deep learning for image/video quality assessment,
restoration and compression.
   - Unsupervised/semi-supervised learning methods that learn to
enhance/compress images/videos with fewer labels.
   - Robust methods trained with domain adaptation or elaborately
designed constraint to learn from noisy labels collected from
real-world data.
   - Compression for compact descriptors, deep features, semantic features.
   - Collaborative or Adversarial Learning for Machine Vision.
   - Scalable and Distributed Architectures or Machine Vision.

Abstract: 09 January 2022
Manuscript: 31 March 2022

Wenhan Yang, Nanyang Technological University, Singapore
Shiqi Wang, City University of Hong Kong, Hong Kong
Wenqi Ren, Chinese Academy of Sciences (CAS), Beijing, China
Sam Kwong, City University of Hong Kong, Hong Kong
Alex Kot, Nanyang Technological University, Singapore


All questions about submissions should be emailed to Wenhan Yang
(ywhlist150578209 at gmail.com), Shiqi Wang (shiqwang at cityu.edu.hk), and
Wenqi Ren (renwenqi at iie.ac.cn).

Website: https://www.frontiersin.org/research-topics/23539/deep-learning-based-image-enhancement-and-compression
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