GPU-Accelerated Adaptive Dictionary Learning and Sparse Representations for Multispectral Image Super-resolution

Barman, Trishna; Deka, Bhabesh; Prasad, A. V. V.

doi:10.1109/indicon52576.2021.9691521

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…In this section, we first introduce the test datasets, i.e., Set5 [36], Set14 [37], BSD100 [38], and Urban100 [39]. Then, we illustrate the training strategy.…”

Section: Experiments Resultsmentioning

confidence: 99%

Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Wang

2022

Mobile Information Systems

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Image superresolution (ISR) is a hot topic. With the success of deep learning, the convolutional neural network-based ISR makes great progress recently. However, most state-of-the-art networks contain millions of parameters and hundreds of layers. It is difficult to apply models realistically. To solve this problem, we propose a Wavelet Sparse Coding-based Lightweight Network for Image Superresolution (WLSR). Our contributions include four aspects. Firstly, to improve the ISR performance, the WLSR utilizes the superiorities of wavelet sparse coding on ISR. Secondly, we take advantage of the dilated convolution to expand receptive fields. In this case, the filters in WLSR can acquire more information than common convolutional filters from input images. Thirdly, to deal with sparse code efficiently, we employ deformable convolution networks to obtain the convolutional kernels that concentrate on nonzero elements. Fourthly, to make WLSR uint8 quantization robust, we take advantage of the Clipped ReLU activation in the end of WLSR and balance the SR performance with running time. Experimental results indicate that, compared with state-of-the-art lightweight models, the WLSR can achieve exceptional performance with few parameters. Moreover, the WLSR contains 30 times fewer parameters than VDSR, but it works better than VDSR on the validation set.

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“…In this section, we first introduce the test datasets, i.e., Set5 [36], Set14 [37], BSD100 [38], and Urban100 [39]. Then, we illustrate the training strategy.…”

Section: Experiments Resultsmentioning

confidence: 99%

Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Wang

2022

Mobile Information Systems

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“…Therefore, Barman et al (2021) proposed a GPU accelerated adaptive dictionary learning and sparse representation for multispectral (MS) image super resolution. For edge preserving, they extracted high frequency characteristics presented in the input low resolution of MS image using Butter which worth low pass.…”

Section: Related Workmentioning

confidence: 99%

Single Image Super Resolution via Coupled Sparse and Low Rank Dictionary Learning

Sahebkheir

Esmaeily²,

Saba³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Limitations in imaging systems and the effects of changes in sensing have caused limitation in acquiring high resolution images such as satellite images and magnetic resonance imaging (MRI). Sparsity can reduce the noises and improve the resolution. Super resolution in medical and satellite imagery is essential because low resolution image analysis is very difficult. Sparsity techniques have significant influence on computer vision specially when the main objective is extracting the meaningful information. The success of sparsity is related to the nature of signals such as image and sound which are naturally sparse because they were founded based on Wavelet and Fourier equations. In this research, we proposed a method for restoring a clear image from the related low-resolution parts of both MRI and satellite images. First, we proposed a widespread structure for learning the couple low rank and sparse main characteristic representation. Combined optimization of the nuclear and L1 norms extracts the total low rank formation and the local patterns lodged in the image. In that case the reconstructed image will be more informative and matrix decomposition problem can recover a noisy observation matrix into an approximation of low rank matrix and a second matrix which contains some low dimensional structure. We assumed that by removing the blur and noise from these images, they would be reconstructed in the highest quality. The proposed method was compared with a variety dictionary learning approaches which addressed super resolution problem, such as tensor sparsity, Generative Bayesian and TV based methods. We demonstrated the results of applied method on MRI and satellite images, showing both visual and psnr improvements. Dealing with complex data in best manner shows the robustness of the proposed method.

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“…As shown in Figure 1, for diagonal pixels, such as the points at the (2i − 1, 2j − 1) and (2i, 2j) pixel positions, if a pixel in the 45 • or 135 • direction satisfies |u 45 • -u 135 • | ≥ T (u θ denotes the θ-directional gradient for pixels), the pixel can be regarded as an edge point, and the direction of the edge on which the pixel is located can then be determined by Equation (6), where u (h) 45 denotes the pixel's 45 • -directional gradient in the HR image.…”

Section: Fundamental Issues and Ideasmentioning

confidence: 99%

“…Many image scaling methods have been proposed recently , and these methods can be divided into two categories, one being sample-based super-resolution reconstruction [4][5][6][7][8][9][10][11][12] and the other being sample-free-based interpolation [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The main difference between the two is that sample-free-based interpolation uses mathematical methods to estimate pixels directly based on the known pixels, whereas super-resolution reconstruction requires training samples to establish a mapping relationship between low-resolution images and high-resolution images before it can use image block-matching and replacement to complete the interpolation.…”

Section: Introductionmentioning

confidence: 99%

Image Interpolation with Regional Gradient Estimation

Jia

Huang

2022

Applied Sciences

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This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

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GPU-Accelerated Adaptive Dictionary Learning and Sparse Representations for Multispectral Image Super-resolution

Cited by 4 publications

References 25 publications

Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Single Image Super Resolution via Coupled Sparse and Low Rank Dictionary Learning

Image Interpolation with Regional Gradient Estimation

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