Saliency object ranking (SOR) has attracted significant attention recently. Previous methods usually failed to ex-plicitly explore the saliency degree-related relationships between objects. In this paper, we propose a novel Domain Separation Graph Neural Network (DSGNN), which starts with separately extracting the shape and texture cues from each object, and builds an shape graph as well as a texture graph for all objects in the given image. Then, we propose a Shape-Texture Graph Domain Separation (STGDS) module to separate the task-relevant and irrelevant information of target objects by explicitly modelling the relationship between each pair of objects in terms of their shapes and textures, respectively. Furthermore, a Cross Image Graph Domain Separation (CIGDS) module is introduced to explore the saliency degree subspace that is robust to different scenes, aiming to create a unified representation for targets with the same saliency levels in different images. Importantly, our DSGNN automatically learns a multi-dimensional feature to represent each graph edge, allowing complex, diverse and ranking-related relationships to be modelled. Experimental results show that our DS-GNN achieved the new state-of-the-art performance on both ASSR and IRSR datasets, with large improvements of 5.2% and 4.1% SA-SOR, respectively. Our code is provided in https://github.com/Wu-ZJ/DSGNN.

Exploiting complementary advantages of different reflection and scattering properties of near-infrared (NIR) images and visible (VIS) images, this letter first proposes a defogging model for single image input and then develops an extended model, a fusion model for NIR and VIS color images, to enhance the visibility of image objects in scattering environments. Our fusion model enhances the extracted details of NIR and VIS images by filtering with our defogging model in the frequency domain that takes into account the energy preservation of these two types of images in addition to the high resolution of the fused results. Finally, based on the initial fusion, we propose a color retention mapping method to keep the fusion results free of color distortion. Experimental results demonstrate that our proposed method not only achieves good defogging effect, but also can effectively combine the complementary NIR and VIS information in image color and visibility.

Pixel-level semantic segmentation is a challenging task with a huge amount of computation, especially if the input sizes are large. In the segmentation network, apart from the pyramid backbone network, an extra decoder network is often employed to recover the spatial detail information. In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the coarse results and residuals of segmentations by decomposing the label at different levels of residual blocks. Specifically speaking, we use the residual features to learn the edges and details, and we also use the top-level feature to learn the coarse segmentation result. At the testing phase, the predicted residuals are used to enhance the details of the coarse segmentation result. Residual learning blocks split the network into several shallow sub-networks by level-wise training, which facilitates the gradient propagation in the RPNet. We then evaluate the proposed method and compare it with the recent state-of-the-art methods on CamVid and Cityscapes datasets. The proposed single-shot segmentation based on the RPNet achieves impressive results with high efficiency on the pixel-level segmentation task.

Spatially regularised discriminative correlation filters (SRDCFs) introduce spatial regularisation weights to mitigate the boundary effects caused by circular convolution which obtains superior performance. However, spatial regularisation is computationally expensive; this limits the real‐time performance of SRDCF. This study proposes high‐speed spatial constraint to DCFs (HSCDCFs) for tracking. Using a large area of the sample to learn a CF, then, the authors introduce the spatial constraint to penalise CF coefficients. Their method formulation allows the CFs to efficiently learn a mass of negative samples and high‐quality positive samples. They perform experiments on two benchmark datasets: OTB‐2013 and OTB‐2015. Compared to SRDCF, they provide a slightly reduce of 2.7 and 3.1%, respectively, in mean overlap precision, their method obtains the real‐time speed of 62.5 fps which is ten times faster than SRDCF.

In view of the shortcoming of traditional image fusion based on discrete wavelet transform (DWT) with unclear textural information, an effective visible light and infrared image fusion algorithm via feature residual and statistical matching is proposed in this study. First, the source images are decomposed into low‐frequency coefficients and high‐frequency coefficients by DWT. Second, two different fusion schemes are designed for the low‐frequency coefficients and high frequency ones, respectively. The low‐frequency coefficients are fused by a local feature residual‐based scheme to achieve adaptive fusion; the high‐frequency coefficients are accomplished by a local statistical matching‐based scheme to extract the edge information effectively. Finally, the fused image is obtained by inverse DWT. Experimental results demonstrate that the proposed method can produce a more accurate fused image, leading to an improved performance compared with existing methods.

Salient object detection remains one of the most important and active research topics in computer vision, with wide-ranging applications to object recognition, scene understanding, image retrieval, context aware image editing, image compression, etc. Most existing methods directly determine salient objects by exploring various salient object features. Here, we propose a novel graph based ranking method to detect and segment the most salient object in a scene according to its relationship to image border (background) regions, i.e., the background feature. Firstly, we use regions/super-pixels as graph nodes, which are fully connected to enable both long range and short range relations to be modeled. The relationship of each region to the image border (background) is evaluated in two stages: (i) ranking with hard background queries, and (ii) ranking with soft foreground queries. We experimentally show how this two-stage ranking based salient object detection method is complementary to traditional methods, and that integrated results outperform both. Our method allows the exploitation of intrinsic image structure to achieve high quality salient object determination using a quadratic optimization framework, with a closed form solution which can be easily computed. Extensive method evaluation and comparison using three challenging saliency datasets demonstrate that our method consistently outperforms 10 state-of-the-art models by a big margin.

Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.

The stimulus response of the classical receptive field (CRF) of neuron in primary visual cortex is affected by its periphery [i.e., non-CRF (nCRF)]. This modulation exerts inhibition, which depends primarily on the correlation of both visual stimulations. The theory of periphery and center interaction with visual characteristics can be applied in night vision information processing. In this paper, a weighted kernel principal component analysis (WKPCA) degree of homogeneity (DH) amended inhibition model inspired by visual perceptual mechanisms is proposed to extract salient contour from complex natural scene in low-light-level image. The core idea is that multifeature analysis can recognize the homogeneity in modulation coverage effectively. Computationally, a novel WKPCA algorithm is presented to eliminate outliers and anomalous distribution in CRF and accomplish principal component analysis precisely. On this basis, a new concept and computational procedure for DH is defined to evaluate the dissimilarity between periphery and center comprehensively. Through amending the inhibition from nCRF to CRF by DH, our model can reduce the interference of noises, suppress details, and textures in homogeneous regions accurately. It helps to further avoid mutual suppression among inhomogeneous regions and contour elements. This paper provides an improved computational visual model with high-performance for contour detection from cluttered natural scene in night vision image.

In region-based stereo matching algorithm, the uniform size of support-window and identical reference value will influence the matching effects. In order to solve these problems, a novel stereo matching algorithm is proposed. The proposed algorithm can adaptively select the size of support-window and distribute weights based on geometrical distance and color distance. Firstly, the support-window size was selected dynamically on the basis of image edge information. Then, the weight model was proposed, according to the change characteristics of matching values. Finally, the dense disparity map was obtained by calculating disparities point-by-point while using sum of weighted color distance as similarity measurement. Experimental results show that this algorithm is fast and efficient, and can effectively reduce the matching noise and improve the matching precision of depth discontinuities and low-textured regions.