Super-Resolution for HD to 4K using Analysis K-SVD dictionary and Adaptive Elastic-Net

Ruangsang, Watchara; Aramvith, Supavadee

doi:10.1109/icdsp.2015.7252044

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Analysis dictionary learning has only seen a handful of applications in the past [33,34]. But wherever they have been used (super-resolution [33], MRI reconstruction [34]), they have surpassed synthesis dictionary learning formulations.…”

Section: B Analysis Co-sparse Codingmentioning

confidence: 99%

Analysis Co-Sparse Coding for Energy Disaggregation

Singh

Majumdar

2019

IEEE Trans. Smart Grid

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Energy disaggregation is the task of segregating the aggregate energy of the entire building (as logged by the smartmeter) into the energy consumed by individual appliances. This is a single channel (the only channel being the smart-meter) blind source (different electrical appliances) separation problem. In recent times dictionary learning based approaches have shown promise in addressing the disaggregation problem. The usual technique is to learn a dictionary for every device and use the learnt dictionaries as basis for blind source separation during disaggregation. Dictionary learning is a synthesis formulation; in this work, we propose an analysis approach. The advantage of our proposed approach is that, the requirement of training volume drastically reduces compared to state-of-the-art techniques. This means that, we require fewer instrumented homes, or fewer days of instrumentation per home; in either case this drastically reduces the sensing cost. Results on two benchmark datasets show that our method produces the same level of disaggregation accuracy as state-of-the-art methods but with only a fraction of the training data.

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Section: B Analysis Co-sparse Codingmentioning

confidence: 99%

Analysis Co-Sparse Coding for Energy Disaggregation

Singh

Majumdar

2019

IEEE Trans. Smart Grid

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“…The K-SVD algorithm has continued to gain increasing attention in recent years due to the proliferation of new applications [29,14,7,18]. Several papers have proposed implementations of the K-SVD algorithm as well as the key step orthogonal matching pursuit (OMP) algorithm.…”

Section: Related Workmentioning

confidence: 99%

Scalable 2D K-SVD parallel algorithm for dictionary learning on GPUs

Hé

Miskell

et al. 2016

Proceedings of the ACM International Conference on Computing Frontiers

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In recent years, the K-SVD algorithm for dictionary learning has been widely used in the field of image processing. The learning algorithm constructs a dictionary consisting of groups of signal atoms derived from a set of images. The sparse linear combination of the signal atoms are used to construct the best possible match based upon the original images. The myriad applications of K-SVD algorithm include reconstruction, compression, denoising, sparse coding, super resolution, and feature extraction. The K-SVD algorithm is a serial machine learning algorithm whereby each of the signal atoms are trained in succession. All of the signal atoms are updated once within any given iteration. Given that the algorithmic complexity for one iteration is O(n 4), the training phase of the K-SVD algorithm is timeintensive. In order to increase the speed the K-SVD algorithm and reduce the run-time execution of each iteration, the following paper proposes a parallel version of the K-SVD algorithm and verifies its validity. We design and optimize the parallel algorithm on an Nvidia Titan X GPU by employing three strategies, specifically batches, early stop, and streaming. Experimental results indicate that the parallel algorithm produces a pronounced speedup of 80× when compared to multi-thread MATLAB implementation of the K-SVD algorithm running on a quad-core CPU.

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“…Many research topics, such as feature preservation in video coding [8], [9] and super-resolution [10], [11], [12], have been published to solve the low-resolution problem. Deep Convolutional Neural Networks (CNN) have recently become a powerful model for solving the ill-posed SR problem [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-FusNet of Cross Channel Network for Image Super-Resolution

2023

Self Cite

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Image Super-resolution (SR) has gained considerable attention in artificial intelligence (AI) research and image-based applications. Recent deep learning-based SR models have demonstrated remarkable accuracy and perceptual quality in the resulting images. However, the computational cost and model parameters are the most challenging limitations in real-world applications. Additionally, designing an efficient and lightweight SR algorithm to improve the perceptual quality of the SR images is a critical issue. According to these considerations, we propose a Multi-FusNet of Cross Channel Network (MFCC) network by modeling a multipath residual network, named multi-RG, with cross-filtering fusion. Additionally, a pixel shuffling fusion technique is used to fuse low-level features into the up-sampled features of the multi-RG. The experimental results show the comparison of the proposed MFCC to the state-of-the-art SR models. The proposed method significantly reduces the number of network parameters (8.4 times compared to RCAN) while preserving the visual quality of the result and achieving the best PSNR value compared to the other state-of-the-art methods.

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Super-Resolution for HD to 4K using Analysis K-SVD dictionary and Adaptive Elastic-Net

Cited by 4 publications

References 11 publications

Analysis Co-Sparse Coding for Energy Disaggregation

Analysis Co-Sparse Coding for Energy Disaggregation

Scalable 2D K-SVD parallel algorithm for dictionary learning on GPUs

Multi-FusNet of Cross Channel Network for Image Super-Resolution

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