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In recent years, single-image super-resolution (SISR) has acquired tremendous progress with the development of deep learning. However, the majority of SISR methods based on deep learning focus on building more complex networks, which inevitably lead to the problems of computational and memory costs. Thus, these methods may fail to be applied in real-world scenarios. To solve this problem, this paper proposes a lightweight convolution network combined with transformer for SISR named as MMSR. Specifically, an efficient convolutional neural network (CNN) based on multi-scale feature fusion is designed for local feature extraction, which is called MFF-CNN. In addition, we propose a simple and efficient multiple attention block (MAB) to further utilize the context information in features. MAB incorporates channel attention and transformer to help network obtain similar features at a long-term dependence, making full use of global information to further refine texture details. Finally, this paper provides comprehensive results for different settings of the entire network. Experimental results on common used datasets demonstrate that the proposed method can achieve better performances at the 2 × , 3 × and 4 × scales than other state-of-the-art lightweight methods. [ABSTRACT FROM AUTHOR]

As an advanced image editing technology, image inpainting leaves very weak traces in the tampered image, causing serious security issues, particularly those based on deep learning. In this paper, we propose the global–local feature fusion network (GLFFNet) to locate the image regions tampered by inpainting based on deep learning. GLFFNet consists of a two-stream encoder and a decoder. In the two-stream encoder, a spatial self-attention stream (SSAS) and a noise feature extraction stream (NFES) are designed. By a transformer network, the SSAS extracts global features regarding deep inpainting manipulations. The NFES is constructed by the residual blocks, which are used to learn manipulation features from noise maps produced by filtering the input image. Through a feature fusion layer, the features output by the encoder is fused and then fed into the decoder, where the up-sampling and convolutional operations are employed to derive the confidential map for inpainting manipulation. The proposed network is trained by the designed two-stage loss function. Experimental results show that GLFFNet achieves a high location accuracy for deep inpainting manipulations and effectively resists JPEG compression and additive noise attacks. [ABSTRACT FROM AUTHOR]

Medical image segmentation has the priori guiding significance for clinical diagnosis and treatment. In the past ten years, a large number of experimental facts have proved the great success of deep convolutional neural networks in various medical image segmentation tasks. However, the convolutional networks seem to focus too much on the local image details, while ignoring the long-range dependence. The Transformer structure can encode long-range dependencies in image and learn high-dimensional image information through the self-attention mechanism. But this structure currently depends on the database scale to give full play to its excellent performance, which limits its application in medical images with limited database size. In this paper, the characteristics of CNNs and Transformer are integrated to propose a dual encoding–decoding structure of the X-shaped network (X-Net). It can serve as a good alternative to the traditional pure convolutional medical image segmentation network. In the encoding phase, the local and global features are simultaneously extracted by two types of encoders, convolutional downsampling, and Transformer and then merged through jump connection. In the decoding phase, a variational auto-encoder branch is added to reconstruct the input image itself in order to weaken the impact of insufficient data. Comparative experiments on three medical image datasets show that X-Net can realize the organic combination of Transformer and CNNs. [ABSTRACT FROM AUTHOR]