Self-attention enhanced deep residual network for spatial image steganalysis. (2023)
Journal Article
XIE, G., REN, J., MARSHALL, S., ZHAO, H., LI, R. and CHEN, R. 2023. Self-attention enhanced deep residual network for spatial image steganalysis. Digital signal processing [online], 139, article 104063. Available from: https://doi.org/10.1016/j.dsp.2023.104063

As a specially designed tool and technique for the detection of image steganography, image steganalysis conceals information under the carriers for covert communications. Being developed on the BOSSbase dataset and released a decade ago, most of the... Read More about Self-attention enhanced deep residual network for spatial image steganalysis..

CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. (2023)
Journal Article
LI, Y., REN, J., YAN, Y., LIU, Q., MA, P., PETROVSKI, A. and SUN, H. 2023. CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing. IEEE transactions on geoscience and remote sensing [online], 61, 5513011. Available from: https://doi.org/10.1109/TGRS.2023.3276589

As a fundamental task in remote sensing observation of the earth, change detection using hyperspectral images (HSI) features high accuracy due to the combination of the rich spectral and spatial information, especially for identifying land-cover vari... Read More about CBANet: an end-to-end cross band 2-D attention network for hyperspectral change detection in remote sensing..

H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification. (2023)
Journal Article
LIU, X., DONG, Z., LI, H., REN, J., ZHAO, H., LI, H., CHEN, W. and XIAO, Z. 2023. H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification. Remote sensing [online], 15(10), article 2497. Available from: https://doi.org/10.3390/rs15102497

Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this pa... Read More about H-RNet: hybrid relation network for few-shot learning-based hyperspectral image classification..

Tensor singular spectral analysis for 3D feature extraction in hyperspectral images. (2023)
Journal Article
FU, H., SUN, G., ZHANG, A., SHAO, B., REN, J. and JIA, X. 2023. Tensor singular spectral analysis for 3D feature extraction in hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 61, article 5403914. Available from: https://doi.org/10.1109/TGRS.2023.3272669

Due to the cubic structure of a hyperspectral image (HSI), how to characterize its spectral and spatial properties in three dimensions is challenging. Conventional spectral-spatial methods usually extract spectral and spatial information separately,... Read More about Tensor singular spectral analysis for 3D feature extraction in hyperspectral images..

Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet. (2023)
Journal Article
CHEN, R., HUANG, H., YU, Y., REN, J., WANG, P., ZHAO, H. and LU, X. 2023. Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet. IEEE internet of things journal [online], 10(18), pages 15966-15979. Available from: https://doi.org/10.1109/JIOT.2023.3268636

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise... Read More about Rapid detection of multi-QR codes based on multistage stepwise discrimination and a compressed mobilenet..

Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images. (2023)
Journal Article
MA, P., REN, J., SUN, G., ZHAO, H., JIA, X., YAN, Y. and ZABALZA, J. 2023. Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 61, article 5508912. Available from: https://doi.org/10.1109/TGRS.2023.3260634

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this stu... Read More about Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images..

Contour extraction of medical images using an attention-based network. (2023)
Journal Article
LV, J.J., CHEN, H.Y., LI, J.W., LIN, K.H., CHEN, R.J., WANG, L.J., ZENG, X.X., REN, J.C. and ZHAO, H.M. 2023. Contour extraction of medical images using an attention-based network. Biomedical signal processing and control [online], 84, article 104828. Available from: https://doi.org/10.1016/j.bspc.2023.104828

A comprehensive analysis of medical images is important, as it assists in early screening and clinical treatment as well as subsequent rehabilitation. In general, the contour information can elaborately describe the shape and size of lesions in a med... Read More about Contour extraction of medical images using an attention-based network..

PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification. (2023)
Journal Article
LIU, Q., XUE, D., TANG, Y., ZHAO, Y., REN, J. and SUN, H. 2023. PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification. Remote sensing [online], 15(4), article 890. Available from: https://doi.org/10.3390/rs15040890

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsup... Read More about PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification..

Multiscale diff-changed feature fusion network for hyperspectral image change detection. (2023)
Journal Article
LUO, F., ZHOU, T., LIU, J., GUO, T., GONG, X. and REN, J. 2023. Multiscale diff-changed feature fusion network for hyperspectral image change detection. IEEE transactions on geoscience and remote sensing [online], 61, article 5502713. Available from: https://doi.org/10.1109/TGRS.2023.3241097

For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is... Read More about Multiscale diff-changed feature fusion network for hyperspectral image change detection..

Attention mechanism enhanced multi-layer edge perception network for deep semantic medical segmentation. (2023)
Journal Article
SUN, M., LI, P., REN, J. and WANG, Z. 2023. Attention mechanism enhanced multi-layer edge perception network for deep semantic medical segmentation. Cognitive computation [online], 15(1), pages 348-358. Available from: https://doi.org/10.1007/s12559-022-10094-4

Existing deep learning–based medical image segmentation methods have achieved gratifying progress, but they still suffer from the coarse boundaries with similar pixels of target. Because the boundary of medical images becomes blurred and the gradient... Read More about Attention mechanism enhanced multi-layer edge perception network for deep semantic medical segmentation..