Quality assessment is a key problem to be resolved in imageprocessing. Few research works have been designed to analyze the quality of images using different techniques. However, the accuracy involved during the proc...
详细信息
Traditional Chinese medicine preparation techniques have limitations in terms of efficiency and quality, which seriously restrict the development of the Chinese medicine industry. This article adopts an intelligent pr...
详细信息
We present Parametric Piecewise Linear networks (PPLNs) for temporal vision inference. Motivated by the neuromorphic principles that regulate biological neural behaviors, PPLNs are ideal for processing data captured b...
In Real-World super-resolution, the intricate degradation of images can result in artifacts or textures that are not sufficiently rich in the generated high-resolution image. Several studies have progressively embrace...
详细信息
ISBN:
(纸本)9798350359329;9798350359312
In Real-World super-resolution, the intricate degradation of images can result in artifacts or textures that are not sufficiently rich in the generated high-resolution image. Several studies have progressively embraced Stable Diffusion as a prior for generating more textured outcomes. Nevertheless, the diffusion model faces challenges in preserving the fidelity of the image during denoising. To tackle this issue, we introduce TargetSR. This framework enhances the network's ability to recognize and localize objects in Real-World degraded images, resulting in the generation of high-resolution images with both reasonable and rich textures. This contributes to improving the semantic and visual fidelity of the images. Our Object-CLIP module identifies and locates objects in an image, retrieves corresponding text, and integrates the encoded text and object location information into the denoising network. This integration enhances the network's effective utilization of information from text features. During the preparation stage, we employ correction-recovery processing to manipulate the degraded images. The correction of degradation enhances the network's capacity to address various types of degradation in the real world, while recovery contributes to enhancing the fidelity of the image during the denoising stage of image generation. Experimental results demonstrate the enhanced effectiveness of our approach in leveraging the textual features of an image to generate textures that are more reasonable and rich.
Recently, deep learning has transformed machine learning by significantly enhancing its artificial intelligence as artificialneuralnetworks (ANN) have become increasingly prevalent. Due of its extensive range of app...
详细信息
Classic image features were once widely used in image classification but have been almost entirely replaced by neuralnetworks today. While the performance of neuralnetworks, especially convolutional neuralnetworks ...
详细信息
Aiming at the problem that the image segmentation accuracy of highway pavement distress is easily affected by complex texture, noisy background, uneven illumination conditions and external environmental interference, ...
详细信息
ISBN:
(纸本)9798350350920
Aiming at the problem that the image segmentation accuracy of highway pavement distress is easily affected by complex texture, noisy background, uneven illumination conditions and external environmental interference, this paper studies the image segmentation methods of highway pavement distress based on semantic segmentation Convolutional neuralnetworks (CNN). Firstly, the methods of the image segmentation highway pavement distress based on FCN-DenseNet, DeepLabv3+, MobileNet are compared and analyzed. Secondly, the four variants of CNN models are investigated for the image segmentation of highway pavement distress, including FCN-DenseNet121 for Pavement Distress Segmentation (FCN-D121-PDS), DeepLabv3-DRN for Pavement Distress Segmentation (DL-D-PDS), DeepLabv3-MobilenetV3 for Pavement Distress Segmentation (DL-M-PDS and DeepLabv3-Mobilenet1 for Pavement Distress Segmentation (DL-M1-PDS). Finally, the comparative experiments were conducted, and the results showed that the average of DL-M1-PDS network is superior to the other three methods, with image segmentation accuracy of 98.20%.
Significant progress has been made in medical image segmentation using deep learning techniques, with the Ushaped architecture being a classic choice. However, effectively capturing and integrating both local features...
详细信息
Significant progress has been made in medical image segmentation using deep learning techniques, with the Ushaped architecture being a classic choice. However, effectively capturing and integrating both local features and remote dependencies remains a key challenge for improving deep learning-based segmentation methods. In this paper, we propose a flexible Rolling Multilayer Perceptron (Rolling-MLP) module to address this issue. Building upon this concept, we present the Rolling-Unet network, which combines the strengths of Multilayer Perceptrons (MLPs) with Convolutional neuralnetworks (CNNs) to efficiently extract and fuse local features and remote dependencies. Furthermore, to explore the potential of Rolling-MLP for two-dimensional medical image segmentation, we propose Rolling-MLP configurations with distinct receptive field shapes (linear and area-shaped) and summarize the influence of Rolling-MLP's key parameters on the shape of receptive fields. We conducted extensive experiments on four datasets, surpassing a variety of state-of-the-art methods in accuracy. Moreover, Rolling-MLP is far ahead in Central processing Unit (CPU) inference speed, indicating its potential in medical cyber-physical systems engineering applications. This paper demonstrates the strong comprehensive ability of Rolling-MLP in two-dimensional medical image segmentation tasks, providing a novel approach for constructing medical image segmentation networks, alternative to CNNs and Transformers.
This paper introduces an artificialneural Network model that integrates advanced deep learning techniques from computer vision and natural language processing domains. The model focuses on automating the captioning p...
详细信息
Malicious code detection is one of the important research directions in the field of cybersecurity. Converting code into image information using convolutional neuralnetworks (CNN) for malicious code detection has bee...
详细信息
暂无评论