• Journal of Information Processing Systems
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• 제19권4호
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• pp.427-438
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• 2023

Plenty of works have indicated that single image super-resolution (SISR) models relying on synthetic datasets are difficult to be applied to real scene text image super-resolution (STISR) for its more complex degradation. The up-to-date dataset for realistic STISR is called TextZoom, while the current methods trained on this dataset have not considered the effect of multi-scale features of text images. In this paper, a multi-scale and attention fusion model for realistic STISR is proposed. The multi-scale learning mechanism is introduced to acquire sophisticated feature representations of text images; The spatial and channel attentions are introduced to capture the local information and inter-channel interaction information of text images; At last, this paper designs a multi-scale residual attention module by skillfully fusing multi-scale learning and attention mechanisms. The experiments on TextZoom demonstrate that the model proposed increases scene text recognition's (ASTER) average recognition accuracy by 1.2% compared to text super-resolution network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.69-77
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• 2017

This paper represents a novel framework for multi-scale image fusion. Multi-scale Kalman Smoothing (MKS) algorithm with quad-tree structure can provide a powerful multi-resolution image fusion scheme by employing Markov property. In general, such approach provides outstanding image fusion performance in terms of accuracy and efficiency, however, quad-tree based method is often limited to be applied in certain applications due to its stair-like covariance structure, resulting in unrealistic blocky artifacts at the fusion result where finest scale data are void or missed. To mitigate this structural artifact, in this paper, a new scheme of multi-scale fusion framework is proposed. By employing Super Resolution (SR) technique on MKS algorithm, fine resolved measurement is generated and blended through the tree structure such that missed detail information at data missing region in fine scale image is properly inferred and the blocky artifact can be successfully suppressed at fusion result. Simulation results show that the proposed method provides significantly improved fusion results in the senses of both Root Mean Square Error (RMSE) performance and visual improvement over conventional MKS algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1477-1491
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• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권9호
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• pp.3186-3203
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• 2021

In this paper, a multi-scale local difference directional number (MLDDN) pattern is proposed for pig identification. Firstly, the color images of individual pig are converted into grey images by the most significant bits (MSB) quantization, which makes the grey values have better discrimination. Then, Gabor amplitude and phase responses on different scales are obtained by convoluting the grey images with Gabor masks. Next, by calculating the main difference of local edge directions instead of traditionally edge information, the directional numbers of Gabor amplitude and phase responses are encoded. Finally, the block histograms of the encoded images are concatenated on each scale, and the maximum pooling is adopted on different scales to avoid the high feature dimension. Experimental results on two pigsties show that MLDDN impressively outperforms the other widely used local descriptors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1888-1906
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• 2024

Aiming at the problems that the edge of melanoma image is fuzzy, the contrast with the background is low, and the hair occlusion makes it difficult to segment accurately, this paper proposes a model MSCNet for melanoma segmentation based on U-net frame. Firstly, a multi-scale pyramid fusion module is designed to reconstruct the skip connection and transmit global information to the decoder. Secondly, the contextural information conduction module is innovatively added to the top of the encoder. The module provides different receptive fields for the segmented target by using the hole convolution with different expansion rates, so as to better fuse multi-scale contextural information. In addition, in order to suppress redundant information in the input image and pay more attention to melanoma feature information, global channel attention mechanism is introduced into the decoder. Finally, In order to solve the problem of lesion class imbalance, this paper uses a combined loss function. The algorithm of this paper is verified on ISIC 2017 and ISIC 2018 public datasets. The experimental results indicate that the proposed algorithm has better accuracy for melanoma segmentation compared with other CNN-based image segmentation algorithms.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• 소성∙가공
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• 제21권6호
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• pp.366-371
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• 2012

Atomistic simulations have become useful tools for exploring new insights in materials science, but the length and time scale that can be handled with atomistic simulations are seriously limiting their practical applications. In order to make meaningful quantitative predictions, atomistic simulations are necessarily combined with higher-scale modeling. The present research is thus concerned with the development of a multi-scale model and its application to the prediction of the mechanical properties of body-centered cubic(BCC) iron with an emphasis on the coupling of atomistic molecular dynamics with meso-scale discrete dislocation dynamics modeling. In order to achieve predictive multi-scale simulations, it is necessary to properly incorporate atomistic details into the meso-scale approach. This challenge is handled with the proposed hierarchical information passing strategy from atomistic to meso-scale by obtaining material properties and dislocation mobility. Finally, this fundamental and physics-based meso-scale approach is employed for quantitative predictions of the mechanical response of single crystal iron.