Super-resolution reconstruction method based on a fused multi-level feature map

A super-resolution reconstruction and feature map technology, applied in the field of computer vision, can solve problems such as inconsistency in subjective evaluation and achieve good accuracy

Active Publication Date: 2019-05-31
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

However, these PSNR-oriented methods tend to output overly smooth results without enough high-frequency d

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  • Super-resolution reconstruction method based on a fused multi-level feature map
  • Super-resolution reconstruction method based on a fused multi-level feature map
  • Super-resolution reconstruction method based on a fused multi-level feature map

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Embodiment Construction

[0029] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0030] Such as figure 1 As shown, a super-resolution reconstruction method based on convolutional neural network, the network model is divided into two parts: feature extraction network and reconstruction network. The feature extraction network can be divided into a feature extraction part and a feature fusion part. The feature extraction part uses t identical convolutional layers, and the feature fusion part consists of a 1×1CNN and a 3×3CNN. The reconstruction network consists of an upsampling operator and a convolutional layer.

[0031] Specific steps are as follows:

[0032] Step 1: Use sequentially connected convolutional layers to extract features from low-resol...

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Abstract

The invention discloses a super-resolution reconstruction method based on fusion of a multi-level feature map, and the method comprises the following steps of employing the idea of a dense network toconstruct a feature extraction network for generating the multi-level feature map; performing dimensionality reduction on the connected feature map by using a convolutional neural network with a convolution kernel size of 1 * 1, fusing the feature map, performing feature extraction of the network on the basis to obtain a fused multi-level feature map, and using a sub-pixel convolutional neural network as an upsampling operator to obtain a high-resolution reconstructed image. In the training process, a perception loss function is used as a minimization target to generate a high-resolution imagemore conforming to visual perception. According to the method, the defect that an existing super-resolution reconstruction algorithm cannot fully utilize a multi-level feature map is overcome, localand overall information in a low-resolution image obtained by a feature extraction network can be fully utilized, and a high-resolution image can be accurately and quickly reconstructed from the low-resolution image.

Description

technical field [0001] The invention relates to a super-resolution reconstruction method based on fusing multi-level feature maps, and belongs to the technical field of computer vision. Background technique [0002] Single image super-resolution (SISR), as a fundamental low-level computer vision problem, has received increasing attention from the research and artificial intelligence communities. The goal of SISR is to recover high-resolution (HR) images from single low-resolution (LR) images. Since the pioneering work of SRCNN was proposed in the literature (Image Super-Resolution Using Deep Convolutional Networks[J].IEEETransactions on Pattern Analysis and Machine Intelligence, 2014, 38(2), the deep convolutional neural network (CNN) method has brought booming. Various network structure designs and training strategies have continuously improved SR performance, especially in the peak signal-to-noise ratio (PSNR) accuracy evaluation index, which has achieved a huge improvem...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06K9/62G06N3/04G06N3/08
CPCY02T10/40
Inventor 杨欣王真真谢堂鑫周大可李志强
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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