Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Ma, Xiaole; Hu, Shaohai; Liu, Shuaiqi; Fang, Jing; Xu, Shuwen

doi:10.3390/electronics8030303

Cited by 17 publications

(13 citation statements)

References 36 publications

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“…Consequently, the proposed approximate adder with the novel hybrid error reduction scheme is found to be highly power-and energy-efficient while also having good computation accuracy. Therefore, our design is highly suitable for application to inherently error-resilient energy-efficient computing, such as DSP, deep learning, and neuromorphic computing [2][3][4]34,35].…”

Section: Resultsmentioning

confidence: 99%

“…This approach is based on the observation that not all applications require 100% computation accuracy. Specifically, many digital signal processing (DSP) applications are inherently error-resilient [2][3][4]. For example, humans may not recognize sporadic errors in digital image processing, such as lossy discrete cosine transform, since they are usually negligible because of human sensory limitations.…”

Section: Introductionmentioning

confidence: 99%

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Design and Analysis of an Approximate Adder with Hybrid Error Reduction

2020

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This paper presents an energy-efficient approximate adder with a novel hybrid error reduction scheme to significantly improve the computation accuracy at the cost of extremely low additional power and area overheads. The proposed hybrid error reduction scheme utilizes only two input bits and adjusts the approximate outputs to reduce the error distance, which leads to an overall improvement in accuracy. The proposed design, when implemented in 65-nm CMOS technology, has 3, 2, and 2 times greater energy, power, and area efficiencies, respectively, than conventional accurate adders. In terms of the accuracy, the proposed hybrid error reduction scheme allows that the error rate of the proposed adder decreases to 50% whereas those of the lower-part OR adder and optimized lower-part OR constant adder reach 68% and 85%, respectively. Furthermore, the proposed adder has up to 2.24, 2.24, and 1.16 times better performance with respect to the mean error distance, normalized mean error distance (NMED), and mean relative error distance, respectively, than the other approximate adder considered in this paper. Importantly, because of an excellent design tradeoff among delay, power, energy, and accuracy, the proposed adder is found to be the most competitive approximate adder when jointly analyzed in terms of the hardware cost and computation accuracy. Specifically, our proposed adder achieves 51%, 49%, and 47% reductions of the power-, energy-, and error-delay-product-NMED products, respectively, compared to the other considered approximate adders.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Analysis of an Approximate Adder with Hybrid Error Reduction

2020

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“…The guided filter is a linear filter, which can better retain the details of the image and avoid the computation depending on the size of the filter [14], [39]- [41]. Assuming that the output image is q and the guide image is I , the local linear relationship between the output image q and the guide image I can be described as in formula (6).…”

Section: B Bilateral Filter and Guided Filtermentioning

confidence: 99%

Multi-Focus Color Image Fusion Algorithm Based on Super-Resolution Reconstruction and Focused Area Detection

Liu

Yin

et al. 2020

IEEE Access

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Multi-focus image fusion is an image processing that generates an integrated image by merging multiple images from different focus area in the same scene. For most fusion methods, the detection of the focus area is a critical step. In this paper, we propose a multi-focus image fusion algorithm based on a dual convolutional neural network (DualCNN), in which the focus area is detected from super-resolved images. Firstly, the source image is input into a DualCNN to restore the details and structure from its superresolved image, as well as to improve the contrast of the source image. Secondly, the bilateral filter is used to reduce noise on the fused image, and the guided filter is used to detect the focus area of the image and refine the decision map. Finally, the fused image is obtained by weighting the source image according to the decision map. Experimental results show that our algorithm can well retain image details and maintain spatial consistency. Compared with existing methods in multiple groups of experiments, our algorithm can achieve better visual perception according to subjective evaluation and objective indexes. INDEX TERMS Multi-focus image fusion, super-resolution, dual convolutional neural network, focus area detection.

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“…After a very careful peer-review process, a total of 32 papers were accepted. These works include SAR/ISAR [2][3][4][5][6][7][8][9], polarimetry [10][11][12], MIMO [13,14], direction of arrival (DOA)/direction of departure (DOD) [13][14][15], sparse sensing [5,14,16], ground-penetrating radar (GPR) [17][18][19], through-wall radar [20,21], coherent integration [22,23], clutter suppression [24,25], and meta-materials, among others [26][27][28][29][30][31]. All of these accepted papers are the latest research results and are expected to be further advanced, applied, and diverted.…”

Section: The Present Issuementioning

confidence: 99%