Fast bilateral filter with spatial subsampling

Yang, Yang; Xiong, Yanyan; Cao, Yabing; Zeng, Lanling; Zhao, Yan; Zhan, Yafeng

doi:10.1007/s00530-022-01004-7

Cited by 3 publications

(5 citation statements)

References 41 publications

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“…It would be also interesting to investigate soft quantization or clustering techniques to approximate or define the affinity matrix in the weighted scheme, so that the results can be free of blocky artifacts without post-processing. Possible ways to achieve this objective can be the approximation of the bilateral affinity with spatial subsampling 21 or the soft clustering affinity. 25 …”

Section: Discussionmentioning

confidence: 99%

“… The proposed method only deals with the details, more studies need to be carried out to investigate the tone manipulation techniques 20 , 53 to further improve the clarity. Our fast solution is based on a hard quantization technique, that is, the bilateral grid, more studies need to be conducted to explore affinity designs with soft quantization 21 or clustering. 25 …”

Section: Discussionmentioning

confidence: 99%

“…Our fast solution is based on a hard quantization technique, that is, the bilateral grid, more studies need to be conducted to explore affinity designs with soft quantization 21 or clustering. 25 …”

Section: Discussionmentioning

confidence: 99%

“…Recent methods for detail enhancement mostly rely on image decomposition with edge-aware smoothing filters, such as the bilateral filter, 20 , 21 anisotropic diffusion filter, 22 guided filter, 23 , 24 soft clustering filter, 25

reconstruction filter, 26

filter, 27 generalized smoothing filter, 28 and weighted least square filter. 29 The decomposition-based methods consider the pixels in the neighborhood during the enhancement process, thus could alleviate the problems of the histogram equalization-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Bian,

Yang

2023

DIGITAL HEALTH

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Background X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive. Method In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results. Results The subjective results indicate that our method achieves higher contrast than the best-performing method ([Formula: see text], [Formula: see text], [Formula: see text]). Furthermore, our method is highly efficient. It takes 0.92 s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images ([Formula: see text], [Formula: see text], [Formula: see text]). Conclusion We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

reconstruction filter, 26

Section: Introductionmentioning

confidence: 99%

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Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Bian,

Yang

2023

DIGITAL HEALTH

Self Cite

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“…Multi-scale image decomposition involves obtaining images with different levels of blurring through filtering. Commonly used decomposition methods include Gaussian filtering, bilateral filtering [37], guided filtering [38], and WLS filtering [39]. However, these filters do not fully address issues related to noise and gradient reversal.…”

Section: Rolling Guidance Filtermentioning

confidence: 99%

Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE

Liu,

Zhou,

Wan

et al. 2023

Sensors

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Infrared sensors capture thermal radiation emitted by objects. They can operate in all weather conditions and are thus employed in fields such as military surveillance, autonomous driving, and medical diagnostics. However, infrared imagery poses challenges such as low contrast and indistinct textures due to the long wavelength of infrared radiation and susceptibility to interference. In addition, complex enhancement algorithms make real-time processing challenging. To address these problems and improve visual quality, in this paper, we propose a multi-scale FPGA-based method for real-time enhancement of infrared images by using rolling guidance filter (RGF) and contrast-limited adaptive histogram equalization (CLAHE). Specifically, the original image is first decomposed into various scales of detail layers and a base layer using RGF. Secondly, we fuse detail layers of diverse scales, then enhance the detail information by using gain coefficients and employ CLAHE to improve the contrast of the base layer. Thirdly, we fuse the detail layers and base layer to obtain the image with global details of the input image. Finally, the proposed algorithm is implemented on an FPGA using advanced high-level synthesis tools. Comprehensive testing of our proposed method on the AXU15EG board demonstrates its effectiveness in significantly improving image contrast and enhancing detail information. At the same time, real-time enhancement at a speed of 147 FPS is achieved for infrared images with a resolution of 640 × 480.

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