Open AccessSOD‐CED: salient object detection for noisy images using convolution encoder–decoder
Author(s) -
Singh Maheep,
Govil Mahesh C.,
Pilli Emmanuel S.,
Vipparthi Santosh Kumar
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5814
Subject(s) - artificial intelligence , computer science , convolutional neural network , convolution (computer science) , benchmark (surveying) , pattern recognition (psychology) , contrast (vision) , noise (video) , computer vision , object detection , noise reduction , salient , deep learning , enhanced data rates for gsm evolution , image (mathematics) , artificial neural network , geodesy , geography
During the last decade, there has been profound progress in the field of visual saliency. However, there still exist various major challenges that hinder the detection performance for scenes with complex composition, presence of additive noise, objects of diverse scale and rotations etc. Generally, images with additive noise have low spatial resolution and blurred edges, which affects the learning capability of the network and causes inaccurate detection. In order to address these issues, in this study, the authors propose a fully convolutional neural network which jointly denoise the input maps by learning edges and contrast details, followed by learning of residing salient details via colour spatial maps in an end‐to‐end fashion. Their framework employs convolutional layers that use gradient and contrast details of images to denoise the areas with high edge density. After denoising, the denoised images are subjected to salient object detection (SOD) using convolutional layers. The effectiveness of the proposed network is evaluated on benchmark datasets. The experimental results demonstrate the significant performance improvement of the proposed method over state‐of‐the‐art detection techniques.