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A Hybrid Degraded Image Enhancement Method Based on Convolutional Neural Networks

A convolutional neural network and degraded image technology, applied in the field of computer vision enhancement, to achieve good enhancement effect, resolution enhancement, denoising enhancement effect

Active Publication Date: 2022-07-19
WUHAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0009] At the problem of existing research method, the present invention research content comprises the following several parts:

Method used

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  • A Hybrid Degraded Image Enhancement Method Based on Convolutional Neural Networks
  • A Hybrid Degraded Image Enhancement Method Based on Convolutional Neural Networks
  • A Hybrid Degraded Image Enhancement Method Based on Convolutional Neural Networks

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Experimental program
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Embodiment Construction

[0035] The present invention can use a computer to perform network training and inference, and is implemented by using a Tensorflow deep learning framework under the Windows operating system. The specific experimental environment configuration is as follows:

[0036] platform Google Colaboratory&Google drive processor Intel(R)Xeon(R)CPU@2.30GHz x2 GPU NVIDIA Tesla P100-PCIE operating system Ubuntu 18.04.3LTS Programming language Python 3.6.9 Deep Learning Framework Tensorflow 1.13, Keras 2.2.5

[0037] The specific implementation is as follows:

[0038] Step 1: Dataset augmentation. The training data set used in the experiment is the image super-resolution reconstruction training set BSDS200, which contains 200 png images of 321x481 size, and the image content includes people, animals, plants, buildings and various natural landscapes. The test data set is the image super-resolution reconstruction test set Set5. Before train...

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Abstract

In order to solve the problem that a single image enhancement model cannot handle mixed degraded images, the present invention provides a mixed degraded image enhancement method based on convolutional neural network, and the method proposes a convolutional neural network-based mixed degraded image enhancement model ——Full convolution sub-pixel residual dense network FCSRDN, which is based on full convolution neural network FCN, through residual connection ResNet and dense connection DenseNet, to complete the feature fusion of the original image at all levels, combined with sub-pixel convolution layer. Upsampling, upscaling the image size, and finally getting an enhanced image. The method of the invention can accept network input of any size, and compared with the existing methods, it can simultaneously complete three enhancements including contrast enhancement, resolution and denoising, and achieves better enhancement effects in all aspects.

Description

technical field [0001] The invention belongs to computer vision enhancement technology, and particularly relates to a convolutional neural network-based image enhancement model for mixed degraded images. Background technique [0002] Image is one of the most important media for information dissemination at present, and it plays an important role in many application fields. However, due to uneven imaging equipment, imaging environment, technical level of photographers, and distortion in the propagation process, a large number of images have degradation phenomena such as low resolution, low brightness, noise, color shift, and distortion. The purpose of image enhancement is to enhance the useful information in the image, improve the image quality, and improve the visual effect of the image. [0003] The traditional image enhancement technology, after a long period of development, has gradually become proficient, and has achieved good performance in many fields. Traditional im...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00G06T3/40G06N3/04G06N3/08
CPCG06T5/002G06T5/007G06T3/4046G06N3/08G06T2207/20081G06T2207/20084G06N3/045
Inventor 曹丽琴宋争光金佳惠李治江
Owner WUHAN UNIV
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