Image rectangling involves filling in the blanks created during image stitching through deformation techniques. However, existing methods still struggle with incomplete filling and distortion of content, ultimately affecting the overall visual impression and potentially hindering subsequent tasks such as recognition. In this work, we design a pixel-wise deformation framework that utilizes explicit edge guidance to maintain consistency of texture and structure, yielding rectangular images with natural structure. Specifically, we decouple motion into region-level and pixel-level components through uniform mesh warping and pixel-wise deformation to precisely rearrange the spatial distribution of all pixels. Uniform deformation preserves local structure within divided patches, while pixel-wise motion coordinates the consistency between patches. Their combination provides robust and accurate pixel-wise offsets for structure-preserved rectangling. To further bolster the consistency of structure and texture, we leverage edge information to establish structural constraints and design an edge-guided enhancement module to aid in restoring fine texture details. Additionally, stitched images encompass both meaningful content and blank spaces, we innovatively incorporate a mask predictor, which acts as a guiding beacon, directing the network's attention solely towards content-rich regions to facilitate precise pixel-wise motion estimation. Experimental results demonstrate that our approach achieves state-of-the-art performance in rectifying irregular boundaries while contributing to downstream visual perception tasks.

As a highly ill-posed issue, single-image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According to the Nyquist sampling theory, the degradation leads to the aliasing effect and makes it hard to restore the correct textures from low-resolution (LR) images. In practice, there are correlations and self-similarities among the adjacent patches in the natural images. This article considers the self-similarity and proposes a hierarchical image super-resolution network (HSRNet) to suppress the influence of aliasing. We consider the SISR issue in the optimization perspective and propose an iterative solution pattern based on the half-quadratic splitting (HQS) method. To explore the texture with local image prior, we design a hierarchical exploration block (HEB) and progressive increase the receptive field. Furthermore, multilevel spatial attention (MSA) is devised to obtain the relations of adjacent feature and enhance the high-frequency information, which acts as a crucial role for visual experience. The experimental result shows that HSRNet achieves better quantitative and visual performance than other works and remits the aliasing more effectively.

Multi-scale design has been considered in recent image super-resolution (SR) works to explore the hierarchical feature information. Existing multi-scale networks aim at building elaborate blocks or progressive architecture for restoration. In general, larger scale features concentrate more on structural and high-level information, while smaller scale features contain plentiful details and textured information. In this point of view, information from larger scale features can be derived from smaller ones. Based on the observation, in this article, we build a sequential hierarchical learning super-resolution network (SHSR) for effective image SR. Specially, we consider the inter-scale correlations of features, and devise a sequential multi-scale block (SMB) to progressively explore the hierarchical information. SMB is designed in a recursive way based on the linearity of convolution with restricted parameters. Besides the sequential hierarchical learning, we also investigate the correlations among the feature maps and devise a distribution transformation block (DTB). Different from attention-based methods, DTB regards the transformation in a normalization manner, and jointly considers the spatial and channel-wise correlations with scaling and bias factors. Experiment results show SHSR achieves superior quantitative performance and visual quality to state-of-the-art methods with near 34% parameters and 50% MACs off when scaling factor is × 4. To boost the performance without further training, the extension model SHSR + with self-ensemble achieves competitive performance than larger networks with near 92% parameters and 42% MACs off with scaling factor ×4.

It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images, which acts as an important role for visual perception of SR results. In this paper, we design a hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner. First, we propose a cross convolution upon traditional edge detectors to localize and represent edge features. Then, cross convolution blocks (CCBs) are designed with feature normalization and channel attention to consider the inherent correlations of features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed the cross convolution blocks and develop the relations of structural features in different scales hierarchically, invoking a lightweight structure-preserving network named as Cross-SRN. Experimental results demonstrate the Cross-SRN achieves competitive or superior restoration performances against the state-of-the-art methods with accurate and clear structural details. Moreover, we set a criterion to select images with rich structural textures. The proposed Cross-SRN outperforms the state-of-the-art methods on the selected benchmark, which demonstrates that our network has a significant advantage in preserving edges.

Single image super-resolution (SISR), as a traditional ill-conditioned inverse problem, has been greatly revitalized by the recent development of convolutional neural networks (CNN). These CNN-based methods generally map a low-resolution image to its corresponding high-resolution version with sophisticated network structures and loss functions, showing impressive performances. This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization. A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization. We first analyze the observation model of image SR problem, inspiring a feasible solution by mimicking and fusing each iteration in a more general and efficient manner. Considering the drawbacks of batch normalization, we propose a feature normalization (F-Norm, FN) method to regulate the features in network. Furthermore, a novel block with FN is developed to improve the network representation, termed as FNB. Residual-in-residual structure is proposed to form a very deep network, which groups FNBs with a long skip connection for better information delivery and stabling the training phase. Extensive experimental results on testing benchmarks with bicubic (BI) degradation show our ISRN can not only recover more structural information, but also achieve competitive or better PSNR/SSIM results with much fewer parameters compared to other works. Besides BI, we simulate the real-world degradation with blur-downscale (BD) and downscale-noise (DN). ISRN and its extension ISRN+ both achieve better performance than others with BD and DN degradation models.

Deinterlacing is a classical issue in video processing area, which aims to generate the progressive video from the interlaced instance. Although numerous algorithms have been proposed in the past decades, their performances are still not satisfactory from both quality of experience and processing efficiency. This paper focuses on the spatial-temporal correlation in the given frame, and design a network for recovering the missing field. Intra-frame motion compensation is considered between the given fields for detail refinement. Furthermore, we address the inherent correlations among image features with channel attention for better exploration. Extensive experimental results on different video sequences show that our method outperforms state-of-the-art methods according to both objective and subjective evaluations satisfying the real-time requirement.

Recognizing freehand sketches with high arbitrariness is such a great challenge that the automatic recognition rate has reached a ceiling in recent years. In this paper, we explicitly explore the shape properties of sketches, which has almost been neglected before in the context of deep learning, and propose a sequential dual learning strategy that combines both shape and texture features. We devise a two-stage recurrent neural network to balance these two types of features. Our architecture also considers stroke orders of sketches to reduce the intra-class variations of input features. Extensive experiments on the TU-Berlin benchmark set show that our method achieves over 90% recognition rate for the first time on this task, outperforming both humans and state-of-the-art algorithms by over 19 and 7.5 percentage points, respectively. Especially, our approach can distinguish the sketches with similar textures but different shapes more effectively than recent deep networks. Based on the proposed method, we develop an on-line sketch retrieval and imitation application to teach children or adults to draw. The application is available as Sketch.Draw.

Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.