KEYWORDS: Convolution, 3D image processing, 3D modeling, Super resolution, Performance modeling, Feature extraction, Lawrencium, 3D image reconstruction, Education and training, Deconvolution
In recent years, significant progress has been made in single-image super-resolution (SISR) with the emergence of convolutional neural networks (CNNs). However, the application of SISR on low computing power devices is hindered by the massive number of parameters and computational costs. Despite the focus on lightweight SISR models in many studies, the majority still struggles to balance performance and model size, making it difficult to apply them in real-life situations. Therefore, we propose to construct an SISR network termed 3D lightweight image super-resolution (3DLSR) network by introducing 3DCNN to this task. By leveraging the additional dimension of 3D convolution, the proposed 3DLSR can extract the interchannel and innerchannel information of color images, thereby aiding the reconstruction of high-resolution images while maintaining a small model size. Furthermore, we redesign a best-fitting network structure for 3DLSR based on the difference between 3D convolution and 2D convolution. The experimental results demonstrate the superiority of our 3DLSR, as it can achieve a competitively quantitative metric with a parameter size one order of magnitude smaller than the majority, compared with the state-of-the-art methods.
KEYWORDS: Simulation of CCA and DLA aggregates, Databases, Principal component analysis, Detection and tracking algorithms, Feature extraction, Optical alignment, Optical engineering, Data modeling, Silicon, Algorithm development
Although discriminative locality alignment (DLA), which is based on the idea of part optimization and whole alignment, has better performance than classical methods in feature extraction, DLA is too overly sensitive to the values of the parameters and falls short of exploiting the full supervision information. We propose a novel supervised feature extraction method, named enhanced discriminative locality alignment (EDLA), for robust feature extraction. EDLA is not sensitive on the choice of the parameters, and both the local structure and class label information are taken into consideration in EDLA algorithm. Moreover, a kernel version of EDLA, named kernel EDLA, is developed through applying the kernel trick to EDLA to increase its performance on nonlinear feature extraction. Experiments on the face databases demonstrate the effectiveness of our methods.
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