Fine-grained scale image super-resolution method based on non-local enhancement network

A network-enhancing and super-resolution technology, applied in image data processing, graphics and image conversion, instruments, etc., can solve problems such as low computing efficiency

Active Publication Date: 2020-06-05
FUZHOU UNIVERSITY
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AI Technical Summary

Problems solved by technology

If a specific model is trained for each positive fine-grained scale factor, it is impossible to store models separately for all fine-grained scale factors in the limited memory space, and it is computationally inefficient

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  • Fine-grained scale image super-resolution method based on non-local enhancement network
  • Fine-grained scale image super-resolution method based on non-local enhancement network
  • Fine-grained scale image super-resolution method based on non-local enhancement network

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Embodiment Construction

[0051] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0052] The present invention provides a fine-grained scale image super-resolution method based on non-local enhancement network, such as figure 1 shown, including the following steps:

[0053] Step A: Preprocessing the original high-resolution training image to obtain an image block pair dataset composed of low-quality high-resolution image blocks of different scales and the original high-resolution training image block, specifically including the following steps:

[0054] Step A1: Perform fine-grained downsampling preprocessing on the high-resolution training image to obtain low-resolution images of different scales. The range of the scale factor is (1, 4], and the value interval is 0.1;

[0055] Step A2: Perform preliminary super-resolution reconstruction on the low-resolution image using the bicubic interpolation method to obtain ...

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Abstract

The invention relates to a fine-grained scale image super-resolution method based on a non-local enhancement network, and the method comprises the following steps: A, carrying out the preprocessing ofan original high-resolution training image, and obtaining an image block pair data set composed of low-quality high-resolution image blocks of different scales and original high-resolution training image blocks; B, training a non-local enhanced deep network for the data set by using the image blocks; C, inputting the high-resolution image of the low-quality test image into a deep network for reconstruction to obtain a super-resolution result. According to the method, a non-local enhanced deep residual structure is used, non-local operation and common convolution are combined, local and non-local image attributes can be effectively captured, image super-resolution is carried out, and compared with an existing super-resolution model, the method can remarkably improve the performance of image super-resolution on the fine-grained scale.

Description

technical field [0001] The invention relates to the fields of image and video processing and computer vision, in particular to a fine-grained scale image super-resolution method based on a non-local enhancement network. Background technique [0002] Image super-resolution is an important topic in digital image processing. In practical application scenarios, limited by the cost of image acquisition equipment, image transmission bandwidth, or the technical bottleneck of the imaging model itself, the quality of the obtained image is often affected, and it cannot become a large-scale high-definition image with sharp edges and no blocky blur . The single-frame image super-resolution algorithm tries to reconstruct a high-resolution image from a low-resolution image without introducing blur, and has been widely used in security monitoring, medical imaging, and satellite aerial images. [0003] Interpolation-based methods proposed earlier can solve the super-resolution problem at ...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06T3/60
CPCG06T3/4053G06T3/60G06T3/4046
Inventor 牛玉贞黄江艺翁涵梅
Owner FUZHOU UNIVERSITY
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