Dark light image denoising method based on dense connection convolution

A dense connection, image technology, applied in image enhancement, image data processing, neural learning methods, etc., can solve problems such as inability to achieve denoising effect, loss of detailed information, and inability to obtain high-quality denoised images, and achieve good denoising. effect of effect

CN110782414APending Publication Date: 2020-02-11FUZHOU UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2020-02-11

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Abstract

The invention relates to a dark light image denoising method based on dense connection convolution, and the method comprises the following steps: constructing a dense connection denoising convolutional neural network model, and carrying out the training of the dense connection denoising convolutional neural network model; acquiring a to-be-processed image, and preprocessing the to-be-processed image to obtain a target to-be-processed image; and processing the target to-be-processed image through the trained dense connection denoising convolutional neural network model to obtain a denoised image. According to the method, a large amount of image detail information can be retained even when an image in a dark light or backlight scene is processed, a high-quality denoised image is obtained, and a good denoising effect is achieved.
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Description

technical field

[0001] The invention relates to the technical field of digital image processing, in particular to a method for denoising dark-light images based on densely connected convolution. Background technique

[0002] Image noise refers to unnecessary or redundant interference information in image data. The existence of noise seriously affects the quality of the image, and noise reduction is particularly important.

[0003] The existing image denoising methods generally use the local information of the image to smooth, or divide the image into blocks of a certain size, and combine two-dimensional image blocks with similar structures to form a three-dimensional image according to the similarity between image blocks. Arrays, and then use the method of joint filtering to process these three-dimensional arrays, and return the processed results to the original image through inverse transformation, so as to obtain a denoised image.

[0004] However, these two image denoisi...

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] It should be pointed out that the following detailed description is exemplary and is intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0030] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combina...