Research on Image Colorization Algorithm Based on Residual Neural Network

Qin, Peng; Cheng, Zirui; Cui, Yuhao; Zhang, Jinjing; Miao, Qiguang

doi:10.1007/978-981-10-7299-4_51

Cited by 8 publications

(10 citation statements)

References 14 publications

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“…In this section, we give a brief review of the residual learning network (ResNet). A comprehensive review on the ResNet can be found in [18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…The second 1 * 1 convolution plays a major role in raising the dimension, which causes the output dimension to return back to 256. The input and output dimensions of the middle m * m convolution fall from the original 256 to 64 by the two 1 * 1 convolutions, which greatly reduces the number of parameters and also increases the module depth [23,24]. In order to boost the denoising performance and improve image denoising recognition as much as possible, the bottleneck residual learning module is used to replace the ordinary residual learning module [25].…”

Section: Related Workmentioning

confidence: 99%

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Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

Chen

Wang

2018

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Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.

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“…In this section, we give a brief review of the residual learning network (ResNet). A comprehensive review on the ResNet can be found in [18][19][20][21][22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

Chen

Wang

2018

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“…Colorization is recent active research yet a difficult subject in the realm of image processing, with the goal of quickly predicting and colorizing grayscale images by analyzing image content with a computer. Existing colorizing algorithms can be classified into three categories depending on the information provided by humans: scribble-based [5][6][7][8][9][10], example-based [11][12][13][14][15], and learning-based [1][2][3][4][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] methods.…”

Section: Introductionmentioning

confidence: 99%

“…Varga and Szirányi [17] utilized the features to predict chroma component for each pixel, while Larsson et al [18] used them to predict the probability distributions of chroma component. [19][20][21][22][23] incorporated semantic labels into the training of colorization models, such as object registration [19][20][21][22] and scene classification [23]. [20][21][22][23] used two parallel CNNs to solve colorization task and semantic task, respectively, and the colorization task exploits the features extracted from the semantic task to improve colorization performance.…”

Section: Introductionmentioning

confidence: 99%

Two-stage pyramidal convolutional neural networks for image colorization

Wei

Kuo

et al. 2021

SIP

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The development of colorization algorithms through deep learning has become the current research trend. These algorithms colorize grayscale images automatically and quickly, but the colors produced are usually subdued and have low saturation. This research addresses this issue of existing algorithms by presenting a two-stage convolutional neural network (CNN) structure with the first and second stages being a chroma map generation network and a refinement network, respectively. To begin, we convert the color space of an image from RGB to HSV to predict its low-resolution chroma components and therefore reduce the computational complexity. Following that, the first-stage output is zoomed in and its detail is enhanced with a pyramidal CNN, resulting in a colorized image. Experiments show that, while using fewer parameters, our methodology produces results with more realistic color and higher saturation than existing methods.

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“…However, due to the subsampled and up-sampling they have used during the image processing, there is a certain degree of information loss. Qin et al [18] used the Residual neural network [19] to extract the detail features, and then combined with the guidance of classification information. Their method has helped to reduce the information loss in some extent, but there are still some problems such as incomplete details coloring and color overflow.…”

Section: Introductionmentioning

confidence: 99%

Image Colorization Algorithm based on Dense Neural Network

Qin¹

2019

IJPE

Self Cite

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In most scenes, color images have richer information than grayscale images. This paper presents a method of grayscale image pseudo coloring that constructed and trained an end-to-end deep learning model based on dense neural network aims to extract all kinds of information and features (such as classification information and detail feature information). Entering a grayscale picture to the trained network can generate a full and vibrant vivid color picture. By constantly training the entire network on a wide variety of data sets, you will get the most adaptable, high-performance pseudo color network. The experiments show that the method proposed has a higher utilization of features and can obtain a satisfactory coloring effect. Compared with the current advanced pseudo color methods, it has also made remarkable improvements, and to a certain extent, the problem during the coloring processing have been improved, such as color overflow, loss of details, low contrast etc.

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Research on Image Colorization Algorithm Based on Residual Neural Network

Cited by 8 publications

References 14 publications

Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

Two-stage pyramidal convolutional neural networks for image colorization

Image Colorization Algorithm based on Dense Neural Network

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