This paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.

/pdf/ntire-2017-challenge-on-single-image-super-resolution-3vppgzkpyi.pdf

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To solve these problems, we propose the very deep residual channel attention networks (RCAN). Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections. Each residual group contains some residual blocks with short skip connections. Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information. Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels. Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods.

Image Super-Resolution Using Very Deep Residual Channel Attention Networks

Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR) However, we observe that deeper networks for image SR are more difficult to train The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs To solve these problems, we propose the very deep residual channel attention networks (RCAN) Specifically, we propose a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections Each residual group contains some residual blocks with short skip connections Meanwhile, RIR allows abundant low-frequency information to be bypassed through multiple skip connections, making the main network focus on learning high-frequency information Furthermore, we propose a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels Extensive experiments show that our RCAN achieves better accuracy and visual improvements against state-of-the-art methods

/pdf/image-super-resolution-using-very-deep-residual-channel-3te0jjlxi2.pdf

Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks, recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark evaluation shows that DRRN significantly outperforms state of the art in SISR, while utilizing far fewer parameters. Code is available at https://github.com/tyshiwo/DRRN_CVPR17.

/pdf/image-super-resolution-via-deep-recursive-residual-network-4npbhz4o8c.pdf

Image Super-Resolution via Deep Recursive Residual Network

Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack highfrequency textures and do not look natural despite yielding high PSNR values.,,We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixelaccurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Edges provide important visual information in scene surfaces. The need for fast and robust feature extraction from 3D data is nowadays fostered by the widespread availability of cheap commercial depth sensors and multi-camera setups. This article investigates the challenge of detecting edges in surfaces represented by unorganized point clouds. Generally, edge recognition requires the extraction of geometric features such as normal vectors and curvatures. Since the normals alone do not provide enough information about the geometry of the cloud, further analysis of extracted normals is needed for edge extraction, such as a clustering method. Edge extraction through these techniques consists of several steps with parameters which depend on the density and the scale of the point cloud. In this paper we propose a fast and precise method to detect sharp edge features by analysing the eigenvalues of the covariance matrix that are defined by each point's k-nearest neighbors. Moreover, we evaluate quantitatively, and qualitatively the proposed methods for sharp edge extraction using several dihedral angles and well known examples of unorganized point clouds. Furthermore, we demonstrate the robustness of our approach in the noisier real-world datasets.

/pdf/fast-and-robust-edge-extraction-in-unorganized-point-clouds-h823v5ifva.pdf

Fast and Robust Edge Extraction in Unorganized Point Clouds

https://upcommons.upc.edu/bitstream/2117/175186/1/postprint-Multi-modal%20deep%20learning%20Fuji%20apple%20detetion%20using%20RGB-D%20cameras%20and%20their%20radiometric%20capabilites.pdf

Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry

A novel real-time algorithm for head and hand tracking is proposed in this paper. This approach is based on data from a range camera, which is exploited to resolve ambiguities and overlaps. The position of the head is estimated with a depth-based template matching, its robustness being reinforced with an adaptive search zone. Hands are detected in a bounding box attached to the head estimate, so that the user may move freely in the scene. A simple method to decide whether the hands are open or closed is also included in the proposal. Experimental results show high robustness against partial occlusions and fast movements. Accurate hand trajectories may be extracted from the estimated hand positions, and may be used for interactive applications as well as for gesture classification purposes.

Javier Ruiz-Hidalgo

Papers

Fast and Robust Edge Extraction in Unorganized Point Clouds

Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry

Real-Time Head and Hand Tracking Based on 2.5D Data

Fruit detection in an apple orchard using a mobile terrestrial laser scanner.