One of the emerging paradigms in the diagnosis and severity assessment of skin disorders, particularly acne, on the face is the use of advanced digital technology (ADT) in skin disease monitoring. It is said to be a very prevalent issue that has to be looked at by experts in this day and age. Nonetheless, the traditional approach to acne diagnosis still relies on the opinions and expertise of medical professionals. There could be fatal outcomes from both delayed and inaccurate diagnoses. Since acne is a condition that directly affects the healthcare system, this study focusses on accelerating the diagnostic process and closing the gap between diagnosis and treatment. In this work, we introduce a smart face acne disease level monitoring device that allows acne sufferers in different geographical locations to track the severity and specifics of their acne and to communicate precautions. Convolutional neural networks, or CNNs, play a major role in this suggested architecture's AI-enabled cloud-based IoT device interconnectivity. Based on a set of photos, CNNs predict the degree of face acne, which could have implications for further study. This proposed study also addresses the influence of age. Geographically speaking, the architecture provides all the domains of skin diagnostic and preventive scheme, notably for acne diagnosis, addressing the present issue faced by patients with limited or no access to e-healthcare services. Graphical Abstract

Multispectral image registration is the process of aligning the spatial regions of two images with different distributions. One of the main challenges it faces is to resolve the severe inconsistencies between the reference and target images. This paper presents a novel multispectral image registration network, Multi-scale Intuitionistic Fuzzy Set Feature-guided Registration Network (IFSrNet), to address multispectral image registration. IFSrNet generates pseudo-infrared images from visible images using Cycle Generative Adversarial Network (CycleGAN), which is equipped with a multi-head attention module. An end-to-end registration network encodes the input multispectral images with intuitionistic fuzzification, which employs an improved feature descriptor—Intuitionistic Fuzzy Set–Scale-Invariant Feature Transform (IFS-SIFT)—to guide its operation. The results of the image registration will be presented in a direct output. For this task we have also designed specialised loss functions. The results of the experiment demonstrate that IFSrNet outperforms existing registration methods in the Visible–IR dataset. IFSrNet has the potential to be employed as a novel image-to-image translation paradigm.

Image inpainting benefits much from the future Internet, but the memory and computational cost in encoding image features in deep learning methods poses great challenges to this field. In this paper, we propose a parallel decoding structure based on GANs for image inpainting, which comprises a single encoding network and a parallel decoding network. By adding a diet parallel extended-decoder path for semantic inpainting (Diet-PEPSI) unit to the encoder network, we can employ a new rate-adaptive dilated convolutional layer to share the weights to dynamically generate feature maps by the given dilation rate, which can effectively decrease the number of convolutional layer parameters. For the decoding network composed of rough paths and inpainting paths, we propose the use of an improved CAM for reconstruction in the decoder that results in a smooth transition at the border of defective areas. For the discriminator, we substitute the local discriminator with a region ensemble discriminator, which can attack the restraint of only the recovering square, like areas for traditional methods with the robust training of a new loss function. The experiments on CelebA and CelebA-HQ verify the significance of the proposed method regarding both resource overhead and recovery performance.

A robust image registration technique for motion correction of chemical exchange saturation transfer (CEST) MRI series was introduced in this paper. The method was based on adaptive stochastic gradient descent optimization (ASGD), and was tested on the motion-corrupted Z-spectral image series of in vivo data of a tumor-bearing mouse brain acquired on an 11.7 Tesla scanner. Compared with conventional gradient descent (GD) optimization for registration, both qualitative and quantitative results indicated that the ASGD method allowed more accurate motion correction for the corrupted Z-spectral image series.

The fusion of multi-polarized petrographic images of rock thin sections involves the fusion of feature information from microscopic images of rock thin sections illuminated under both plane-polarized and orthogonal-polarized light. During the fusion process of rock thin section images, the inherent high resolution and abundant feature information of the images pose substantial challenges in terms of computational complexity when dealing with massive datasets. In engineering applications, to ensure the quality of image fusion while meeting the practical requirements for high-speed processing, this paper proposes a novel fast fusion Transformer. The model leverages a soft matching algorithm based on intuitionistic fuzzy sets to merge redundant tokens, effectively mitigating the negative effects of asymmetric dependencies between tokens. The newly generated artificial tokens serve as brokers for the Query (Q), forming a novel lightweight fusion strategy. Both subjective visual observations and quantitative analyses demonstrate that the Transformer proposed in this paper is comparable to existing fusion methods in terms of performance while achieving a notable enhancement in its inference efficiency. This is made possible by the attention paradigm, which is equivalent to a generalized form of linear attention, and the newly designed loss function. The model has been experimented on with multiple datasets of different rock types and has exhibited robust generalization capabilities. It provides potential for future research in diverse geological conditions and broader application scenarios.