WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution

• URL: http://arxiv.org/abs/2010.03199v1
• Date: Wed, 7 Oct 2020 06:15:11 GMT
• Title: WDN: A Wide and Deep Network to Divide-and-Conquer Image Super-resolution
• Authors: Vikram Singh (1), Anurag Mittal (1) ((1) Indian Institute of Technology - Madras)
• Abstract summary: Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. We propose an approach to divide the problem of image super-resolution into multiple sub-problems and then solve/conquer them with the help of a neural network.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Divide and conquer is an established algorithm design paradigm that has proven itself to solve a variety of problems efficiently. However, it is yet to be fully explored in solving problems with a neural network, particularly the problem of image super-resolution. In this work, we propose an approach to divide the problem of image super-resolution into multiple sub-problems and then solve/conquer them with the help of a neural network. Unlike a typical deep neural network, we design an alternate network architecture that is much wider (along with being deeper) than existing networks and is specially designed to implement the divide-and-conquer design paradigm with a neural network. Additionally, a technique to calibrate the intensities of feature map pixels is being introduced. Extensive experimentation on five datasets reveals that our approach towards the problem and the proposed architecture generate better and sharper results than current state-of-the-art methods.

Related papers

• Adaptive Convolutional Neural Network for Image Super-resolution [43.06377001247278]
  We propose a adaptive convolutional neural network for image super-resolution (ADSRNet) The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information.
  arXiv  Detail & Related papers  (2024-02-24T03:44:06Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• Image Super-resolution with An Enhanced Group Convolutional Neural Network [102.2483249598621]
  CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. We present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture. Experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR.
  arXiv  Detail & Related papers  (2022-05-29T00:34:25Z)
• NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis [53.106414896248246]
  We present a framework that allows analysts to effectively build the solution sub-graph space and guide the network search by injecting their domain knowledge. Applying this technique in an iterative manner allows analysts to converge to the best performing neural network architecture for a given application.
  arXiv  Detail & Related papers  (2020-09-28T01:48:45Z)
• NAS-DIP: Learning Deep Image Prior with Neural Architecture Search [65.79109790446257]
  Recent work has shown that the structure of deep convolutional neural networks can be used as a structured image prior. We propose to search for neural architectures that capture stronger image priors. We search for an improved network by leveraging an existing neural architecture search algorithm.
  arXiv  Detail & Related papers  (2020-08-26T17:59:36Z)
• Cascade Convolutional Neural Network for Image Super-Resolution [15.650515790147189]
  We propose a cascaded convolution neural network for image super-resolution (CSRCNN) Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images.
  arXiv  Detail & Related papers  (2020-08-24T11:34:03Z)
• On the unreasonable effectiveness of CNNs [7.673853485227739]
  Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems. In an attempt to put upper bounds on the capability of baseline CNNs for solving image-to-image problems we applied a widely used standard off-the-shelf network architecture (U-Net) to the "inverse problem" of XOR decryption from noisy data and show acceptable results.
  arXiv  Detail & Related papers  (2020-07-29T11:16:20Z)
• Binary Neural Networks: A Survey [126.67799882857656]
  The binary neural network serves as a promising technique for deploying deep models on resource-limited devices. The binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. We present a survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error.
  arXiv  Detail & Related papers  (2020-03-31T16:47:20Z)
• Super-resolution of multispectral satellite images using convolutional neural networks [0.0]
  We propose a method to train state-of-the-art CNNs using pairs of lower-resolution multispectral and high-resolution pan-sharpened image tiles. The derived quality metrics show that the method improves information content of the processed images.
  arXiv  Detail & Related papers  (2020-02-03T07:06:36Z)
• Multimodal Deep Unfolding for Guided Image Super-Resolution [23.48305854574444]
  Deep learning methods rely on training data to learn an end-to-end mapping from a low-resolution input to a high-resolution output. We propose a multimodal deep learning design that incorporates sparse priors and allows the effective integration of information from another image modality into the network architecture. Our solution relies on a novel deep unfolding operator, performing steps similar to an iterative algorithm for convolutional sparse coding with side information.
  arXiv  Detail & Related papers  (2020-01-21T14:41:53Z)
• Learned Multi-View Texture Super-Resolution [76.82725815863711]
  We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. Our architecture unifies the concepts of (i) multi-view super-resolution based on the redundancy of overlapping views and (ii) single-view super-resolution based on a learned prior of high-resolution (HR) image structure.
  arXiv  Detail & Related papers  (2020-01-14T13:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.