Color image denoising method based on bit layering and attention fusion mechanism

Abstract

The invention relates to a color image denoising method based on a bit layering and attention fusion mechanism, comprising: step 1, performing bit layering on the color image with noise, and dividing it into 24 channels; step 2, performing bit layering The final image is input into the noise estimation network, and a channel attention module is added to each layer of the network to obtain the noise estimation of the noisy color image; step 3, the image after noise estimation and bit layering is spliced, input into the encoding network, and Add a channel attention module to each layer of the network to assign different weight values ​​to the features of different channels; step 4, input the output of the encoding network into the decoding network, and add the output of each layer of the network to the spatial attention module and The corresponding layers of the decoding network are spliced, and then convolution and deconvolution operations are performed; step 5, the output of the decoding network is sent to the convolution layer, and the final output is a clean image after denoising.

Description

technical field [0001] The invention relates to the technical field of computer vision images, in particular to a color image denoising method based on a bit layering and attention fusion mechanism. Background technique [0002] Image is an indispensable information carrier in daily life and plays an important role in the process of people acquiring, storing and transmitting information. With the continuous development of digital multimedia technology, computer images have been widely used in medical imaging, pattern recognition, and target detection. However, in the process of image acquisition, transmission and storage, it is inevitable to be disturbed by various noises. Image noise refers to unnecessary or redundant interference information existing in image data. People refer to various redundant signals that affect people's reception of image information as noise signals. Image noise blurs the image, and sometimes even conceals image features, and at the same time adv...

AI Technical Summary

Problems solved by technology

However, the above deep learning method does not take into account: the difference of high and low frequency information storage data in the image and the influence of the noise value in the image on the denoising effect. Therefore, the above deep learning based method has achieved relatively good results in image denoising. limited effect

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