Image steganography algorithm based on image colorization

Bi, Xinliang; Wu-jun, Yao; Zhang, Zhuo; Huang, Siyuan; Liu, Jia; Chen, Bo-Wei

doi:10.1117/12.2600415

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The main innovations of this method are as follows: one is to train a deep neural network to directly predict the mapping from grayscale images with colored points to color images; second, the network will also provide users with a data-driven color palette, suggesting the ideal color of the gray map in a given location. This approach can also bring the benefit of reducing the workload for users, and it can also calculate the global histogram of a color reference map to color the gray map [ 10 , 33 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic Gray Image Coloring Method Based on Convolutional Network

Fan

Xie

2022

Computational Intelligence and Neuroscience

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Image coloring is a time-consuming and laborious work. For a work, color collocation is an important factor to determine its quality. Therefore, automatic image coloring is a topic with great research significance and application value. With the development of computer hardware, deep learning technology has achieved satisfactory results in the field of automatic coloring. According to the source of color information, this paper can divide automatic coloring methods into three types: image coloring based on prior knowledge, image coloring based on reference pictures, and interactive coloring. The coloring method can meet the needs of most users, but there are disadvantages such as users cannot get the multiple objects in a picture of different reference graph coloring. Aiming at this problem, based on the instance of color image segmentation and image fusion technology, the use of deep learning is proposed to implement regional mixed color more and master the method. It can be divided into foreground color based on reference picture and background color based on prior knowledge. In order to identify multiple objects and background areas in the image and fuse the final coloring results together, a method of image coloring based on CNN is proposed in this paper. Firstly, CNN is used to extract their semantic information, respectively. According to the extractive semantic information, the color of the designated area of the reference image is transferred to the designated area of the grayscale image. During the transformation, images combined with semantic information are input into CNN model to obtain the content feature map of grayscale image and the style feature map of reference image. Then, a random noise map is iterated to make the noise map approach the content feature map as a whole and the specific target region approach the designated area of the style feature map. Experimental results show that the proposed method has good effect on image coloring and has great advantages in network volume and coloring effect.

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Section: Related Workmentioning

confidence: 99%

Automatic Gray Image Coloring Method Based on Convolutional Network

Fan

Xie

2022

Computational Intelligence and Neuroscience

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“… Dave et al (2022) developed a new temporal contrastive learning framework comprising local–local and local–global temporal contrastive loss to encourage the features to be distinct across the temporal dimension. Generative model-based approaches usually use some generative tasks as pretext tasks to learn features, such as image reconstruction ( Fan et al, 2022 ), image inpainting ( Quan et al, 2022 ), image coloring ( Bi et al, 2021 ), etc. In this work, we use image inpainting and slice index prediction as pretext tasks to learn better representations of input modalities as detailed in Sec.…”

Section: Related Workmentioning

confidence: 99%

A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis

Zhou

Zou

2022

Front. Genet.

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Magnetic resonance (MR) imaging plays an important role in medical diagnosis and treatment; different modalities of MR images can provide rich and complementary information to improve the accuracy of diagnosis. However, due to the limitations of scanning time and medical conditions, certain modalities of MR may be unavailable or of low quality in clinical practice. In this study, we propose a new multimodal MR image synthesis network to generate missing MR images. The proposed model comprises three stages: feature extraction, feature fusion, and image generation. During feature extraction, 2D and 3D self-supervised pretext tasks are introduced to pre-train the backbone for better representations of each modality. Then, a channel attention mechanism is used when fusing features so that the network can adaptively weigh different fusion operations to learn common representations of all modalities. Finally, a generative adversarial network is considered as the basic framework to generate images, in which a feature-level edge information loss is combined with the pixel-wise loss to ensure consistency between the synthesized and real images in terms of anatomical characteristics. 2D and 3D self-supervised pre-training can have better performance on feature extraction to retain more details in the synthetic images. Moreover, the proposed multimodal attention feature fusion block (MAFFB) in the well-designed layer-wise fusion strategy can model both common and unique information in all modalities, consistent with the clinical analysis. We also perform an interpretability analysis to confirm the rationality and effectiveness of our method. The experimental results demonstrate that our method can be applied in both single-modal and multimodal synthesis with high robustness and outperforms other state-of-the-art approaches objectively and subjectively.

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“…As a result, scientists are also interested in grey picture colouring technologies. Using colour images as a reference, the authors of papers [1]- [3] propose a new automatic colouring method for line art images that can be applied to areas of a similar colour. Researchers use deep convolutional neural networks (DCNNs) and propose many methods for automatic colouring of line draught images, inspired by the successful application of generative models in image synthesis tasks in recent years; however, the colouring results of these methods are not controllable and are often accompanied by colour artefacts.…”

Section: Introductionmentioning

confidence: 99%

An approach to pseudocoloring of grey scale image using deep learning technique

Srivastava

Gogia²,

Rohith³

2023

J. Phys.: Conf. Ser.

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Image pseudo colorization is the process of adding RGB colours to grayscale images to make them more appealing. Deep learning technology has made progress in the field of automatic colouring. In general, we divide automatic colouring methods into three groups based on where the colour information comes from: colouring based on what you already know and on reference pictures. The colouring method can meet the needs of most users, but there are some drawbacks. For example, users can’t colour different reference graphs for the different things in a picture. In order to solve this problem by recognising several objects and background regions in a picture and combine the final colouring results, the proposed method uses the deep learning approach that regional mixed colours be used more and the method be mastered by using deep learning. Qualitative results (visual perception) validate the effectiveness of pseudocolorisation which split into foreground colour based on a reference picture and background colour based on prior knowledge. Quantitative results such as Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Image Matching Error and Entropy validates the effectiveness of strong edge strength, visually appealing quality and retention of maximum information without disturbing quality of image.

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Image steganography algorithm based on image colorization

Cited by 3 publications

References 11 publications

Automatic Gray Image Coloring Method Based on Convolutional Network

Automatic Gray Image Coloring Method Based on Convolutional Network

A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis

An approach to pseudocoloring of grey scale image using deep learning technique

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