No-reference low-illumination image enhancement method based on deep convolutional neural network

A neural network and deep convolution technology, applied in the field of image processing, can solve the problems of network and loss function design, loss of image detail information, etc.

Active Publication Date: 2021-06-08
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Another big problem facing low-light image enhancement is how to avoid the noise in the original image from being amplified synchronously due to enhancement.
In this regard, there are methods to use image denoising technology as an enhanced pre-processing...

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  • No-reference low-illumination image enhancement method based on deep convolutional neural network
  • No-reference low-illumination image enhancement method based on deep convolutional neural network
  • No-reference low-illumination image enhancement method based on deep convolutional neural network

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Embodiment Construction

[0040] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0041] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a no-reference low-illumination image enhancement method based on a deep convolutional neural network, and belongs to the field of image processing. The method comprises the following steps: firstly, constructing a feature extraction module comprising two branches by using a deep convolutional neural network, and extracting an irradiation component and a reflection component from an input low-illumination image; and then, denoising the reflection component, and integrating the denoised reflection component into an optimization network to obtain an optimized reflection component; then, inputting the irradiation component into the optimization network to obtain an optimized irradiation component; and finally, multiplying the optimized irradiation component by the reflection component to obtain a final enhancement result. According to the method, the reflection component extracted from the input image is fully utilized, the interference of noise in the image is effectively reduced, and the detail expression capability is improved.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a no-reference low-illuminance image enhancement method based on a deep convolutional neural network. Background technique [0002] Low-light environments generally refer to poor lighting conditions, such as cloudy, nighttime, and indoor scenes. In addition, imaging noise is inevitably introduced during the imaging process of the camera. These noises will be further exacerbated by low-illumination conditions, so it is very necessary to properly enhance and denoise low-illumination images. [0003] At present, low-light image enhancement methods can be mainly divided into traditional methods and deep learning methods. The representative ones in the traditional methods are the method based on histogram equalization and the method based on Retinex theory. Stretching of dynamic range. The method based on the Retinex theory mainly starts from the Retinex theory, and extracts the illu...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06N3/04G06N3/08
CPCG06T5/007G06T5/002G06N3/084G06T2207/20081G06T2207/20084G06N3/045Y02T10/40
Inventor 陈勇陈东刘焕淋金曼莉汪波
Owner CHONGQING UNIV OF POSTS & TELECOMM
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