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Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
Hua Li, Yuheng Jia, Runmin Cong +3
Superpixel segmentation aims at dividing the input image into some representative regions containing pixels with similar and consistent intrinsic properties, without any prior know…
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Qijian Zhang, Runmin Cong, Chongyi Li +5
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains a…
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Qijian Zhang, Runmin Cong, Junhui Hou +2
Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue…
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
Feng Li, Runmin Cong, Huihui Bai +3
Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak remo…
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
Chongyi Li, Runmin Cong, Chunle Guo +4
The diverse spatial resolutions, various object types, scales and orientations, and cluttered backgrounds in optical remote sensing images (RSIs) challenge the current salient obje…
NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal
Chongyi Li, Huazhu Fu, Runmin Cong +2
Retinal images have been widely used by clinicians for early diagnosis of ocular diseases. However, the quality of retinal images is often clinically unsatisfactory due to eye lesi…