Article
Computer Science, Information Systems
Junhua Wang, Yuan Jiang, Yongjun Qi, Yongping Zhai
Summary: The proposed algorithm utilizes panchromatic image and nonnegative dictionary learning technology to learn high and low resolution dictionary pairs, achieving remote sensing image fusion through sparse representation and reconstruction, effectively preserving spectral information of multispectral images.
Article
Computer Science, Artificial Intelligence
Usman Haider, Muhammad Hanif, Ahmar Rashid, Syed Fawad Hussain
Summary: This paper proposes a dictionary-based training method for ConvNets that reduces training time significantly while maintaining accuracy by exploiting redundancy in the training data. Experimental results on three publicly available datasets show a 4.5 times reduction in computational burden compared to state-of-the-art algorithms like ResNet-{18,34,50}, with comparable accuracy.
IMAGE AND VISION COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Chang Wang, Yang Wu, Yi Yu, Jun Qiang Zhao
Summary: In this study, an improved multi-modality image fusion method was proposed by combining the joint patch clustering-based adaptive dictionary and sparse representation to address the issue of gray inconsistency caused by the maximum L-1 norm fusion rule. Through quantitative evaluation and comparative experiments, it was demonstrated that the method has superiority in fusion metrics, image quality, and edge preservation.
MACHINE VISION AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Sumit Budhiraja, Rajat Sharma, Sunil Agrawal, Balwinder S. Sohi
Summary: An efficient image fusion method based on sparse representation with clustered dictionary is proposed in this paper for infrared and visible images. By enhancing the edge information of visible image using a guided filter and using non-subsampled contourlet transform for fusion, the proposed method is able to outperform other conventional image fusion methods.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Jinn Ho, Wen-Liang Hwang
Summary: This paper investigates the issue of ambiguity in dictionary estimation and the sensitivity of true sparse vectors and signals to dictionary perturbations in compressed sensing. The study shows that inherent ambiguity in dictionary estimation cannot be resolved even when the desired sparse vector can be obtained. By imposing conditions on the perturbation of the true dictionary, the sparse vector and signal can be stably estimated within the bounds of the dictionary perturbation.
Article
Engineering, Electrical & Electronic
Minghong Xie, Jiaxin Wang, Yafei Zhang
Summary: This paper presents a unified framework for image fusion and completion, achieving separation and restoration of different components through a low-rank and sparse dictionary learning model to recover lost information of damaged images. The maximum l(1)-norm fusion scheme is adopted to merge coding coefficients of different components. Experimental results demonstrate that this method excels in preserving image brightness and details, outperforming other methods in performance evaluation.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Computer Science, Artificial Intelligence
Jin Li, Zilong Liu
Summary: This paper proposes a low-dimensional visual representation convolution neural network (LVR-CNN) for efficient post-transform-based image compression in high-resolution imaging of an on-orbit optical camera. The LVR-CNN transforms the wavelet domain from a large-scale representation to a new wavelet version with a small scale, optimizing compression performance and calculation efficiency. Experimental results show that the proposed LVR-CNN post-transform-based compression method outperforms conventional methods by increasing the peak-signal-noise-ratio (PSNR) by 1.2 to 2.7 dB, indicating its efficiency for remote sensing images.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Yue Pan, Tianye Lan, Chongyang Xu, Chengfang Zhang, Ziliang Feng
Summary: This paper reviews the recent advances in pixel-level image fusion based on convolutional sparse representation (CSR) and discusses the future trends of CSR-based image fusion.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Biomedical
Leiner Barba-J, Lorena Vargas-Quintero, Jose A. Calder PRIMEon-Agudelo
Summary: This paper introduces a transform-based fusion scheme for bone SPECT/CT image analysis, using the Hermite transform for image feature coding. Two different fusion strategies were designed based on coefficient content, and the final fused image was recovered using the inverse transform.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Chemistry, Multidisciplinary
Gihwan Lee, Yoonsik Choe
Summary: This paper proposes an extension of a sparse orthonormal transform based on unions of orthonormal dictionaries for image compression. The method constructs dictionaries into a discrete cosine transform and an orthonormal matrix, and adapts Bayesian optimization to determine a trade-off parameter for efficient implementation and optimal parameter selection.
APPLIED SCIENCES-BASEL
(2022)
Article
Genetics & Heredity
Yanping Li, Nian Fang, Haiquan Wang, Rui Wang
Summary: In this paper, a multi-modal medical image fusion algorithm based on geometric algebra sparse representation is proposed. The algorithm avoids the loss of correlation between channels and outperforms existing methods in subjective and objective quality evaluation.
FRONTIERS IN GENETICS
(2022)
Article
Geochemistry & Geophysics
Shao Xiang, Qiaokang Liang, Leyuan Fang
Summary: High-ratio image compression is a difficult task for remote sensing images due to their complex backgrounds and weak correlation between features. This study proposes a novel entropy model (DWTGMM) based on discrete wavelet transform (DWT) and Gaussian mixture model (GMM) to enhance the representation ability of compression models and estimate the probability distributions of latent representations. Experimental results show that the proposed method achieves excellent performance in remote sensing image compression.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Information Systems
Ankita Vaish, Saumya Patel
Summary: This paper proposes a sparse representation based compression method for fused images using MultiResolution Singular Value Decomposition (MSVD), which can identify significant and less significant details. The significant information is fused using the absolute maximum rule, while the less significant information is fused using sparse representation. The fused images are compressed using different coding techniques. The proposed technique is superior to some related work, as demonstrated by comparisons.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Optics
Muhammad Rafiq Abuturab, Ayman Alfalou
Summary: A new method for multiple color image fusion, compression, and encryption using compressive sensing, chaotic-biometric keys, and optical fractional Fourier transform is proposed in this paper. The proposed cryptosystem has advantages of reduced data storage, uniqueness of biometric keys in CBPMs, very sensitive orders of the FrFT, and a single-channel hybrid optoelectronic setup.
OPTICS AND LASER TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yidong Peng, Weisheng Li, Xiaobo Luo, Jiao Du, Yi Gan, Xinbo Gao
Summary: A novel integrated spatio-temporal-spectral fusion framework is proposed based on semicoupled sparse tensor factorization to generate high-resolution images by blending multisource observations. The method effectively exploits relationships across different domains and can handle multicomplementary spatial, temporal, and spectral information of remote sensing data based on a single unified model. Experiments demonstrate the effectiveness and efficiency of the proposed method in various data fusion scenarios.
INFORMATION FUSION
(2021)
