Color medical images better reflect a patient’s lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can’t be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.

Image super-resolution reconstruction is one of the methods to improve resolution by learning the inherent features and attributes of images. However, the existing super-resolution models have some problems, such as missing details, distorted natural texture, blurred details and too smooth after image reconstruction. To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features and obtain multi-scale feature information. This paper also designs the channel and spatial attention block (CSAB), which is combined with the enhanced residual dense block (ERDB) to extract multi-level depth feature information and enhance feature reuse. In addition, the multi-scale feature information extracted under the three-branch path is fused with global features, and sub-pixel convolution is used to restore the high-resolution image. The experimental results show that the objective evaluation index of MARDGAN on multiple benchmark datasets is higher than other methods, and the subjective visual effect is better. This model can effectively use the original image information to restore the super-resolution image with clearer details and stronger authenticity.

Existing image inpainting algorithms have achieved good results in regular mask inpainting tasks, but there are still many limitations for irregular mask, and the inpainting results lack edge consistency and semantic correctness. To address these problems, we designed a progressive generative adversarial network in this paper. The generative network mainly contains a progressive feature generation module and an adaptive consistent attention module, and the discriminator network uses SN-PatchGAN. The network performs recursive inpainting from the edge to the centre, and the repaired features are used as the conditions for the next feature generation, which makes the constraints of the central content gradually strengthened. In order to obtain distant information from the feature maps as well as to consider the possible problem of inconsistency between feature mappings under different recursions when using attention directly in the progressive inpainting network, we designed an adaptive consistent attention module in this paper, which can adaptively combine scores obtained from different recursions to capture more feature information. The discriminator used in this paper is SN-PatchGAN, which directly calculates the hinge loss of each point on the output graph and can focus on different locations as well as different semantics. Comparing this paper's method on CelebA dataset and CMP Facade dataset with the latest work, the experimental results show that this paper's method performs well in the irregular mask image inpainting task, and the inpainting results have better performance in terms of edge consistency, semantic correctness, and overall image structure.

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network‐based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG‐19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model’s perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people’s requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients’ condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.