Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Li, Liangliang; Si, Yujuan; Jia, Zhenhong

doi:10.3390/a10040116

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Currently, various methods have been proposed for LLIE, including histogram equalization (HE) [5,6], non-local means filtering [7], Retinex-based methods [8,9], multi-exposure fusion [10][11][12], and deep-learning-based methods [13][14][15], among others. While these approaches have achieved remarkable progress, two main challenges impede their practical deployment in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

Chen,

Wen,

Liu

et al. 2023

Electronics

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In this paper, we propose an end-to-end low-light image enhancement network based on the YCbCr color space to address the issues encountered by existing algorithms when dealing with brightness distortion and noise in the RGB color space. Traditional methods typically enhance the image first and then denoise, but this amplifies the noise hidden in the dark regions, leading to suboptimal enhancement results. To overcome these problems, we utilize the characteristics of the YCbCr color space to convert the low-light image from RGB to YCbCr and design a dual-branch enhancement network. The network consists of a CNN branch and a U-net branch, which are used to enhance the contrast of luminance and chrominance information, respectively. Additionally, a fusion module is introduced for feature extraction and information measurement. It automatically estimates the importance of corresponding feature maps and employs adaptive information preservation to enhance contrast and eliminate noise. Finally, through testing on multiple publicly available low-light image datasets and comparing with classical algorithms, the experimental results demonstrate that the proposed method generates enhanced images with richer details, more realistic colors, and less noise.

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Section: Introductionmentioning

confidence: 99%

DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

Chen,

Wen,

Liu

et al. 2023

Electronics

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“…The representatives of the filters include bilateral filtering [1,2], non-local means filtering [3] and guided image filtering [4][5][6]. These filters are used in various applications, such as image denoising [3,7], high dynamic range imaging [8], detail enhancement [9][10][11], free viewpoint image rendering [12], flash/no-flash photography [13,14], up-sampling/super resolution [15,16], alpha matting [5,17], haze removal [18], optical flow and stereo matching [19], refinement processing in optical flow and stereo matching [20,21] and coding noise removal [22,23].…”

Section: Introductionmentioning

confidence: 99%

Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures

2018

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In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing.

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“…4 The traditional spatial domain methods are histogram equalization, 5 gamma correction, 6 and linear stretching transformation et al 4 The frequency domain methods are wavelet transform, 7,8 curvelet transform, 9 contourlet transform et al 10 The contourlet transform is using the laplacian pyramid decomposition (LP) and directional filter bank (DFB) to realize the multiresolution image representation. At present, contourlet transform has been widely used in image fusion and enhancement, and has achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

Jia

2018

AIP Advances

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In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients, and the improved adaptive threshold is adopted to suppress the noise of the high-frequency sub-bands coefficients. Third, the processed coefficients are reconstructed with the inverse NSST. Finally, the unsharp filter is used to enhance the details of the reconstructed image. Experimental results on various microscopy mineral images demonstrated that the proposed approach has a better enhancement effect in terms of objective metric and subjective metric.

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Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain

Cited by 9 publications

References 35 publications

DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures

Microscopy mineral image enhancement based on improved adaptive threshold in nonsubsampled shearlet transform domain

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