SOD‐CED: salient object detection for noisy images using convolution encoder–decoder. Issue 6 (30th August 2019)

• Record Type: Journal Article
• Authors: Singh, Maheep; Govil, Mahesh C.; Pilli, Emmanuel S.; Vipparthi, Santosh Kumar
• Is Part Of: IET computer vision. Volume 13:Issue 6(2019)
• Page Start: 578
• Page End: 587
• Publication Date: 2019-08-30
• Subjects: image segmentation -- convolutional codes -- object detection -- learning (artificial intelligence) -- image denoising -- convolutional neural nets; SOD-CED -- salient object detection -- noisy images -- convolution encoder–decoder -- profound progress -- visual saliency -- detection performance -- complex composition -- additive noise -- diverse scale -- low spatial resolution -- blurred edges -- learning capability -- fully convolutional neural network -- input maps -- contrast details -- salient details -- colour spatial maps -- convolutional layers -- high edge density -- image denoising; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5814
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.