A single-frame image super-resolution reconstruction method based on pre-amplified non-negative neighborhood embedding

A technology of neighborhood embedding and pre-amplification, used in image enhancement, image data processing, instrumentation, etc., can solve problems such as relying on edge detection, resulting image artifacts, etc.

Active Publication Date: 2017-06-16
XIDIAN UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method relies on edge detection, and incorrect edge detection can lead to artifacts in the resulting image

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A single-frame image super-resolution reconstruction method based on pre-amplified non-negative neighborhood embedding
  • A single-frame image super-resolution reconstruction method based on pre-amplified non-negative neighborhood embedding
  • A single-frame image super-resolution reconstruction method based on pre-amplified non-negative neighborhood embedding

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach

[0075] see figure 1 , the specific embodiment of the present invention is as follows:

[0076] Step 1, construct a high-resolution training image set

[0077] (1a) Collect multiple color high-resolution natural images;

[0078] (1b) Use the function rgb2ycbcr in the experimental software Matlab to convert the high-resolution natural image from the red, green, blue RGB color space to the brightness, blue chroma, red chroma YCbCr color space;

[0079] (1c) Collect all brightness images as a high-resolution training image set in denotes the pth high-resolution brightness image, and n denotes the number of images.

[0080] step 2, yes Perform blurring and downsampling operations to obtain a temporary low-resolution image set

[0081] (2a) Yes Each image in is blurred using a Gaussian blur kernel with a variance of 1.1 and a size of 7×7;

[0082] (2b) Then take pixels at intervals of the image to obtain a temporary low-resolution image set after downsampling 3 times...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a single-frame image super-resolution reconstruction method based on pre-amplification non-negative neighborhood embedding, the steps of which are: constructing a high-resolution training image set; blurring and down-sampling it to obtain a temporary low-resolution image set; Pre-enlarge the temporary low-resolution image set by 2 times to obtain a low-resolution training image set; construct a low-resolution training image block set; construct a high-resolution training image block set; pre-amplify the input low-resolution image by 2 times; construct a low-resolution The high-resolution input image block set; the non-negative neighborhood embedding represents the low-resolution input image block set, and obtains the reconstruction coefficient; uses the high-resolution training image block set and the obtained coefficient to obtain a high-resolution output image. The present invention utilizes the non-local similarity of image blocks to propose a new method for constructing training samples, and at the same time uses non-negative neighborhood embedding to effectively solve the problem of selecting the number of neighbors K. Experimental simulations show that the image reconstructed by the invention has clear edges and rich textures, and is closer to real images.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a single-frame image super-resolution reconstruction method based on neighborhood embedding, which can be widely used in the fields of remote sensing reconnaissance, traffic and safety monitoring, pattern recognition and the like. Background technique [0002] The higher the resolution of the image, the more informative it is. High-resolution images play an important role in a wide variety of practical applications. High-resolution satellite images are helpful for target recognition, and high-resolution images are also required in fields such as traffic and safety monitoring, and pattern recognition. Due to the physical conditions of the imaging system and the weather, there are often degradation processes such as motion blur, downsampling, and noise in the imaging process, resulting in low resolution and poor quality of the actual image. To obtain high-res...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 宁贝佳彭羊平高新波许洁高传清
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap