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Hyperspectral image Intensity Adaptive Destriping Method Based on reference image-Guided Pixel Clustering

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Dong, Tengteng Wang, Mi Pan, Jun Wu, Qianyu Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

Hyperspectral images (HSIs) have widespread applications in geoscience, environmental monitoring, and resource management. However, in practical engineering applications, random stripe noise in HSIs severely affects data quality and accuracy, impacting the subsequent use of HSIs. In this article, we propose an HSI intensity adaptive destriping method based on reference image-guided pixel clustering. The proposed method removes stripe noise through three main stages. First, the HSI is analyzed to manually select a specific band image with minimal impact from random stripe noise. This image is designated the initial reference image, and its specific location is identified. Second, the proposed pixel threshold classification method, deep maximum interclass variance (DMIV), is used for pixel threshold classification for band images that are contaminated with random stripe noise and adjacent to the reference image. Finally, the resulting pixels are classified into two categories according to noise intensity: high intensity (HIN) and low intensity (LIN). The proposed intensity adaptive grayscale value reconstruction algorithm is used to remove stripe noise from each pixel category, and the denoised image is updated as a new reference image. Starting from the initial reference image, these steps are repeated along the spectrum to achieve destriping of all bands. We compare the proposed method against traditional and deep learning methods using real HSIs. The experimental results show that after denoising of the image using our method, both visual quality and quantitative evaluation metrics are significantly improved, with particularly excellent stripe removal performance observed for HSIs with significant grayscale variation.

关键词： Destriping hyperspectral images (HSIs) max- imum interclass variance method max- imum interclass variance method reference image spatial clustering spatial clustering spatial clustering

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reference-based image super-resolution with attention extraction and pooling of residuals

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-27页

作者： Du, Junkun Wang, Mingqing Wang, Xin Yang, Zhipeng Li, Xiaojie Wu, Xi Chengdu Univ Informat Technol Coll Comp Sci & Technol Chengdu Peoples R China Tsinghua Univ Dept Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing Peoples R China SUNY Albany AI Plus Inst Coll Integrated Hlth Sci Albany NY USA

reference-based image super-resolution aims to improve the clarity of input low-resolution (LR) images by leveraging additional high-resolution reference (Ref) images. Although existing methods effectively address the problems associated with super-resolution reconstruction based on reference images, there are still significant challenges in effectively bridging the gap between LR and Ref and enhancing the finesse of texture details. This paper presents MARP-SR, which utilizes attention and residual pooling techniques to bridge their resolution gaps while enhancing texture details. The key innovations include the pooled residual feature enhancement (PRFE) module and the attention extraction feature (AEF) module. PRFE employs residual learning through convolution and pooling to boost texture and salient high-frequency features. AEF utilizes attention mechanisms to extract multi-scale features from LR and Ref images, aligning their resolutions. Experiments on benchmark datasets (CUFED5, Urban100, Manga109, and WR-SR) demonstrate that our proposed MARP-SR achieves state-of-the-art performance, generating more realistic and natural-looking super-resolution results than previous methods.

关键词： Super-resolution reference image Attention extraction feature Pooled residual feature enhancement

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reference image-Assisted Auxiliary Feature Fusion in image Inpainting

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 1394-1398页

作者： Bai, Shaojie Lin, Lixia Hu, Zihan Cao, Peng Beijing Inst Graph Commun Sch Informat Engn Beijing 102600 Peoples R China

image inpainting has achieved remarkable advancement in improving image quality via reconstructing the corrupted area with perceptual rationality. However, when the key semantic features of the mask area are blurred, existing methods solely relying on the corrupted image surrounding information may not be sufficient to restore the precise semantics of the original image. To solve this problem, in this letter, we propose a dual codec network guided by reference images, which obtains the effective semantic priors to reconstruct the lost detail feature of the corrupted image. Our network first aligns the reference image using a SIFT aligned module, then, extracts the texture feature and the gradient detail through a Fourier texture restoration module and an auxiliary feature extraction module, respectively, which finally emerge for image inpainting. To validate our scheme, we introduce the Reimage3K dataset with more realistic and diverse scenarios than existing datasets. Comprehensive experiments show that our scheme outperforms SOTA on PSNR, FID, and LPIPS by 3.29 dB, 54.5%, and 51.3% on DPED10K and 3.74 dB, 48.4%, 55.8% on Reimag3 k, respectively.

关键词： Auxiliary feature extraction image inpainting reference image SIFT alignment

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Respiratory motion correction for liver contrast-enhanced ultrasound by automatic selection of a reference image

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MEDICAL PHYSICS 2019年第11期46卷 4992-5001页

作者： Zhang, Ji Zhang, Yanrong Chen, Juan Ling, Gonghao Wang, Xiangyu Xu, Haibo Wuhan Univ Zhongnan Hosp Dept Radiol Wuhan 430071 Peoples R China Huazhong Univ Sci & Technol Tongji Med Coll Union Hosp Dept Ultrasound Wuhan 430070 Peoples R China Stanford Univ Dept Radiol Neuroradiol Sect Stanford CA 94305 USA

PurposeRespiratory motion correction is necessary for the quantitative analysis of liver contrast-enhanced ultrasound (CEUS) image sequences. Most respiratory motion correction methods are based on the dual mode of CEUS image sequences, including contrast and grayscale image sequences. Due to free-breathing motion, the acquired two-dimensional (2D) ultrasound cine might show the in-plane and out-of-plane motion of tumors. The registration of an entire 2D ultrasound contrast image sequence based on out-of-plane images is ineffective. For the respiratory motion correction of CEUS sequences, the reference image is usually considered the standard for the deletion of any out-of-plane images. Most methods used for the selection of the reference image are subjective in nature. Here, a quantitative selection method for an optimal reference image from CEUS image sequences in the B mode and contrast mode was explored. MethodsThe original high-dimensional ultrasound grayscale image data were mapped into a two-dimensional space using Laplacian Eigenmaps (LE), and K-means clustering was adopted. The center image of the larger cluster with a near-peak contrast intensity was considered the optimal ultrasound reference image. In the ultrasound grayscale image sequence, the images with the maximum correlations to the reference image in the same time interval were selected as the corrected image sequence. The effectiveness of this proposed method was then validated on 18 CEUS cases of VX2 tumors in rabbit livers. ResultsCorrection smoothed the time-intensity curves (TICs) extracted from the region of interest of the CEUS image sequences. Before correction, the average of the total mean structural similarity (TMSSIM) and the average of the mean correlation coefficient (MCC) from the image sequences were 0.45 0.11 and 0.67 +/- 0.16, respectively, and after correction, the average TMSSIM and MCC increased (P < 0.001) by 31% to 0.59 0.11 and by 21% to 0.81 +/- 0.11, respecti

关键词： contrast-enhanced ultrasound manifold learning reference image respiratory motion VX2 tumor

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A (k, n) threshold partial reversible AMBTC-based visual cryptography using one reference image

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2019年 59卷 550-562页

作者： Wu, Xiaotian Chen, Dong Yang, Ching-Nung Yang, Yi-Yun Jinan Univ Dept Comp Sci Guangzhou Guangdong Peoples R China Nanjing Univ Informat Sci & Technol Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Shoufeng Township Taiwan

Recently, Yang et al. introduced a (k;n) approach called Reversible Absolute moment block truncation coding Visual Cryptography Scheme (RAVCS) (Yang et al., 2017) to conceal a secret image into n AMBTC shares. However, a large number of reference images is used in their method. To reduce the number of reference images, a (k;n) PRAVCS using one AMBTC reference image is introduced. In encoding phase, a binary secret image is shared into n AMBTC shadow images according to the base matrices generated by two proposed constructions. In decoding phase, the secret image is recovered by stacking sufficient bitmaps, and the AMBTC reference image is partially recovered as well. When n AMBTC shares are used, losslessly reconstruction for the reference image is obtained. Theoretical analysis and experiments by the proposed method are demonstrated to show the effectiveness. Moreover, Construction 1 has larger contrast, while Construction 2 achieves higher reversibility for more thresholds. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Visual cryptography Block truncation coding Partial reversible Meaningful share Contrast reference image

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Change detection approaches for flood extent mapping: How to select the most adequate reference image from online archives?

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2012年第1期19卷 205-213页

作者： Hostache, R. Matgen, P. Wagner, W. CRP Gabriel Lippmann L-4422 Belvaux Luxembourg TU Vienna A-1040 Vienna Austria

Synthetic Aperture Radar images are routinely used for delineating flooded areas. Processing algorithms are often based on change detection techniques that enable a comparison of backscattering signals between the flood image and a reference image. However, as of today, there is little guidance on how to rapidly and reliably extract the most adequate reference image from an online archive. Our study proposes a method that allows the automatic and objective identification of the best reference image with respect to a given flood image. The proposed method consists of two processing steps. First, a subset of archived candidate images acquired on the same track, with the same polarization and in the same period of year as the flood image is created. Next, site-specific time series of regional backscattering values are established and the effects of flooding on the backscattering behaviour are statistically evaluated. We propose two complementary anomaly indexes and their combination in a single index as a means to identify the most adequate reference image for flooding-related change detection applications. The reliability of the proposed method is demonstrated in three representative case studies targeting the flood prone areas of the Severn River (United Kingdom), the Red River (United States) and the Meghna River (Bangladesh). (C) 2012 Elsevier B.V. All rights reserved.

关键词： SAR image Change detection reference image Floods

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On Creation of reference image for Quantitative Evaluation of image Thresholding Method

On Creation of Reference Image for Quantitative Evaluation o...

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10th International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2011)

作者： Shaikh, Soharab Hossain Maiti, Asis Kumar Chaki, Nabendu Univ Calcutta Kolkata 700009 India

ISBN: (纸本)9783642272448

A good reference image is important for relative performance analysis of different image thresholding techniques in a quantitative manner. There exist standard methods for building reference images for document image binarization. However, a gap is found for graphic images referencing. This paper offers six different techniques for building reference images. These may be used for comparing different image thresholding techniques. Experimental results illustrate the relative performance of five different image thresholding methods for the six reference image building methods on a set of ten images taken from the USC-SIPI database. The results would help picking up the right reference image for evaluating binarization techniques.

关键词： reference image image thresholding image binarization quantitative evaluation majority voting misclassification error relative foreground area error

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Respiratory motion correction of liver contrast-enhanced ultrasound sequences by selecting reference image automatically

Respiratory motion correction of liver contrast-enhanced ult...

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Conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Zhang Ji Zhang Yan-Rong Chen Juan Chen Xiao-Hui Zhong Xiao-Li Wuhan Univ Zhongnan Hosp Dept Med Imaging Wuhan 430070 Peoples R China Huazhong Univ Sci & Technol Tongji Med Coll Union Hosp Dept Ultrasound Wuhan 430070 Peoples R China

ISBN: (数字)9781510607248

ISBN: (纸本)9781510607231;9781510607248

Objective Respiratory motion correction is necessary to quantitative analysis of liver contrast-enhance ultrasound (CEUS) image sequences. However, traditionally manual selecting reference image would affect the accuracy of the respiratory motion correction. Methods First, the original high-dimensional ultrasound gray-level image data was mapped into a two-dimensional space by using Laplacian Eigenmaps (LE). Then, the cluster analysis was adopted using K-means, and the optimal ultrasound reference image could be gotten for respiratory motion correction. Finally, this proposed method was validated on 18 CEUS cases of VX2 tumor in rabbit liver, and the effectiveness of this method was demonstrated. Results After correction, the time-intensity curves extracted from the region of interest of CEUS image sequences became smoother. Before correction, the average of total mean structural similarity (TMSSIM) and the average of mean correlation coefficient (MCC) from image sequences were 0.45 +/- 0.11 and 0.67 +/- 0.16, respectively. After correction, the two parameters were increased obviously(P<0.001), and were 0.59 +/- 0.11 and 0.81 +/- 0.11, respectively. The average of deviation valve (DV) from image sequences before correction was 92.16 +/- 18.12. After correction, the average was reduced to one-third of the original value. Conclusions The proposed respiratory motion method could improve the accuracy of the quantitative analysis of CEUS by using the reference image based on the traditionally manual selection. This method is operated simply and has a potential in clinical application.

关键词： contrast-enhanced ultrasound reference image respiratory motion manifold learning VX2 tumor

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Defect Detection of Aerial images without reference image

Defect Detection of Aerial images without reference image

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12th International Conference on Control, Automation and Systems (ICCAS)

作者： Sun, In-sun Jeong, Hong POSTECH Dept Elect Engn Pohang 790784 South Korea

ISBN: (纸本)9788993215045

currently, many different defect detection algorithms have developed. Most finds defect using optical method, and some using image processing uses a reference image. There are several problems using the reference image. The scale, illumination or orientation difference between the reference and the target image make it hard or even impossible to find defect by comparison. Also, there might not be a reference image existed from first place. In this paper, we present a method that automatically find and analyze the defects from an aerial image obtained from a commercial mask inspection device, Aerial image Measurement System (AIMS). By using this, we developed the process create a reference image from the test image with defect, compare the test image with reference image, and find the exact defect position. Our system finds the defect fully automatically in a short time with high accuracy.

关键词： defect detection aerial image AIMS reference image mask defect

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Defect detection in ID cards with accurately reconstructed reference image 20

Defect detection in ID cards with accurately reconstructed r...

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5th International Conference on Multimedia and image Processing (ICMIP)

作者： Chen, Xue Cao, Jian-Wen Wang, Yu-Peng Chinese Acad Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9781450376648

ID card is made by hot-pressing a standard film with identifiable information onto a fixed baseboard with background of wavy lines. In this paper, we propose a defect detect algorithm by synthesising the film image and baseboard image to accurately reconstruct a reference image for a test card. First, to ensure the content consistency in position and scale, we align the card to a standard film image through perspective transformation(PT) based on AKAZE key-points. Besides, we use contrast limited adaptive histogram equalization(CLAHE) to enhance the background pattern of a baseboard image, and then align it to the rectified card. Second, we apply multiply algorithm to synthesise the aligned film image and baseboard image as a reconstructed reference image. Besides, we align the lightness histogram of the reference image to a test card so as to eliminate the lighting difference. Finally, we apply the difference image method based on canny edge detection to detect difference between a reference image with a card, and further extract the defect information. We experiment on cards with different types of defects and shooting disturbances. Results show high accuracy of our method.

关键词： defect detection reference image AKAZE lightness histogram difference image

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