True Wide Convolutional Neural Network for Image Denoising

The analysis of art design under improved convolutional neural network based on the Internet of Things technology

This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportunities for art design education, while deep learning and improved CNN models can provide more accurate and effective tools for image processing and analysis. In order to enhance the effectiveness of art design teaching and students' creative expression, this work proposes an improved CNN model. In model construction, it increases the number of convolutional layers and neurons, and incorporates the batch normalization layer and dropout layer to enhance feature extraction capabilities and reduce overfitting. Besides, this work creates an experimental environment using IoT technology, capturing art image samples and environmental data using cameras, sensors, and other devices. In the model application phase, image samples undergo preprocessing and are input into the CNN for feature extraction. Sensor data are concatenated with image feature vectors and input into the fully connected layers to comprehensively understand the artwork. Finally, this work trains the model using techniques such as cross-entropy loss functions and L2 regularization and adjusts hyperparameters to optimize model performance. The results indicate that the improved CNN model can effectively acquire art sample data and student creative expression data, providing accurate and timely feedback and guidance for art design education and teaching, with promising applications. This work offers new insights and methods for the development of art design education.

GlobalSR: Global context network for single image super-resolution via deformable convolution attention and fast Fourier convolution

Vision Transformer have achieved impressive performance in image super-resolution. However, they suffer from low inference speed mainly because of the quadratic complexity of multi-head self-attention (MHSA), which is the key to learning long-range dependencies. On the contrary, most CNN-based methods neglect the important effect of global contextual information, resulting in inaccurate and blurring details. If one can make the best of both Transformers and CNNs, it will achieve a better trade-off between image quality and inference speed. Based on this observation, firstly assume that the main factor affecting the performance in the Transformer-based SR models is the general architecture design, not the specific MHSA component. To verify this, some ablation studies are made by replacing MHSA with large kernel convolutions, alongside other essential module replacements. Surprisingly, the derived models achieve competitive performance. Therefore, a general architecture design GlobalSR is extracted by not specifying the core modules including blocks and domain embeddings of Transformer-based SR models. It also contains three practical guidelines for designing a lightweight SR network utilizing image-level global contextual information to reconstruct SR images. Following the guidelines, the blocks and domain embeddings of GlobalSR are instantiated via Deformable Convolution Attention Block (DCAB) and Fast Fourier Convolution Domain Embedding (FCDE), respectively. The instantiation of general architecture, termed GlobalSR-DF, proposes a DCA to extract the global contextual feature by utilizing Deformable Convolution and a Hadamard product as the attention map at the block level. Meanwhile, the FCDE utilizes the Fast Fourier to transform the input spatial feature into frequency space and then extract image-level global information from it by convolutions. Extensive experiments demonstrate that GlobalSR is the key part in achieving a superior trade-off between SR quality and efficiency. Specifically, our proposed GlobalSR-DF outperforms state-of-the-art CNN-based and ViT-based SISR models regarding accuracy-speed trade-offs with sharp and natural details.