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Highlights • Propose a new super-resolution method based on CSC and CNN. • Use CNN to reconstruct the high resolution image for the low-frequency part. • Propose an ACSC to reconstruct high resolution image for high-frequency part. • Integrate and develope the advantages of the CSC method and CNN. Abstract The convolutional sparse coding-based super-resolution (CSC-SR) method has shown its good performance in single image super-resolution. It divides the low-resolution (LR) image into low-frequency part and the high-frequency part, and reconstructs their corresponding high-resolution (HR) image with bicubic interpolation and convolutional sparse coding (CSC) method, respectively. This paper is devoted to improve the performance of CSC-SR method. As convolutional neural network (CNN) can reveal the mapping relation between the LR image and the HR image for the low-frequency part better, we replace the bicubic interpolation with CNN to reconstruct the HR image for the low-frequency part. In addition, we propose an adaptive CSC method to reconstruct the HR image for the high-frequency part. We name our proposed super-resolution method as hybrid adaptive convolutional sparse coding-based super-resolution (HACSC-SR) method. Many comparison experiments illustrate that our proposed HACSC-SR method is superior to CSC-SR, CNN as well as several existing super-resolution methods. [ABSTRACT FROM AUTHOR]

We study the problem of 3D object detection from RGB-D images so as to achieve localization (i.e., producing a bounding box around the object) and classification (i.e., determining the object category) simultaneously. Its challenges arise from high intra-class variability, illumination change, background clutter and occlusion. To solve this problem, we propose a novel solution that integrates the 2D information (RGB images), the 3D information (RGB-D images) and the object/scene context information together, and call it the Context-Assisted 3D (C3D) method. In the proposed C3D method, we first use a convolutional neural network (CNN) to jointly detect a 3D object in a scene and its scene category. Then, we improve the detection result furthermore with a Conditional Random Field (CRF) model that incorporates the object potential, the scene potential, the scene/object context, the object/object context, and the room geometry. Extensive experiments are conducted to demonstrate that the proposed C3D method achieves the state-of-the-art performance for 3D object detection against the SUN RGB-D benchmark dataset. [ABSTRACT FROM AUTHOR]

An approach that extracts global attributes from outdoor images to facilitate geometric layout labeling is investigated in this work. The proposed Global-attributes Assisted Labeling (GAL) system exploits both local features and global attributes. First, by following a classical method, we use local features to provide initial labels for all super-pixels. Then, we develop a set of techniques to extract global attributes from 2D outdoor images. They include sky lines, ground lines, vanishing lines, etc. Finally, we propose the GAL system that integrates global attributes in the conditional random field (CRF) framework to improve initial labels so as to offer a more robust labeling result. The performance of the proposed GAL system is demonstrated and benchmarked with several state-of-the-art algorithms against a popular outdoor scene layout dataset. [ABSTRACT FROM AUTHOR]