Recently, deep neural networks have been widely used to approximate or improve image operators. In general, an image operator has some hyper-parameters that change its operating configurations, e.g., the strength of smoothing, up-scale factors in super-resolution, or a type of image operator. To address varying parameter settings, an image operator taking such parameters as its input, namely a parameterized image operator, is an essential cue in image processing. Since many types of parameterization techniques exist, a comparative analysis is required in the context of image processing. In this paper, we therefore analytically explore the operation principles of these parameterization techniques and study their differences. In addition, performance comparisons between image operators parameterized by using these methods are assessed experimentally on common image processing tasks including image smoothing, denoising, deblocking, and super-resolution.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2019 Jun|
|Event||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States|
Duration: 2019 Jun 16 → 2019 Jun 20
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019|
|Period||19/6/16 → 19/6/20|
Bibliographical noteFunding Information:
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2014-3-00077, Development of global multi-target tracking and event prediction techniques based on real-time large-scale analysis)
© 2019 IEEE.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering