A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform

Xie, Xin; Xu, Yin; Liu, Qing; Hu, Fengping; Jiang, Nan; Xiong, Huandong

doi:10.1007/s12652-015-0319-2

Cited by 25 publications

(5 citation statements)

References 17 publications

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“…Image splicing mainly includes registration and fusion. (12) Although a large number of studies are focused on rapid image splicing, (13)(14)(15) the splicing process requires a large number of computations and a large amount of time owing to a large number of images and a significant amount of image overlapping. (11) Therefore, to improve the efficiency of building a tile pyramid, many studies focus on the optimization of the parallel strategy of tile cutting.…”

Section: Building Methods Based On Image Fusionmentioning

confidence: 99%

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Liu¹,

Sun²

2023

Sensors and Materials

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The efficient building of tile pyramids is the basis for achieving efficient visualization of massive remote sensing images and network publishing. With the increasing number of images and amount of data from images, the existing method leads to excessive overall time consumption and low efficiency of the building process due to the poor quality of building algorithms and parallel strategies. In this study, an optimization strategy for building a tile pyramid of massive remote sensing images was proposed. This method comprehensively used the hierarchical blank tiles of remote sensing images and the strategy of multithreading parallel pyramid construction to reorganize the existing tile pyramid construction process. The experimental results showed that the method proposed in this study was less time-consuming and had an obviously improved overall efficiency compared with that of the existing method of building a tile pyramid using massive remote sensing images. Therefore, this method is more advantageous to use than previous methods.

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Section: Building Methods Based On Image Fusionmentioning

confidence: 99%

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Liu¹,

Sun²

2023

Sensors and Materials

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“…In the 2D methods, Karimi et al [ 4 ] and Xie et al [ 5 ] used a combination of scale-invariant feature transform (SIFT) features and Kanade-Lucas-Tomasi (KLT) trackers to obtain background information, but they were time consuming and could only eliminate single or small object. Shene et al [ 6 ] used speeded up robust features (SURF) cascade and random sample consensus (RANSAC) [ 7 , 8 ] to obtain background information.…”

Section: Introductionmentioning

confidence: 99%

Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Zhang

et al. 2021

Sensors

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Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don’t extend the framework of 2D video stabilization but add a “plug and play” module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points’ motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.

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“…Then, it goes through an encoding phase that reduces the information in the image [17]. Mosaics based on compression and wavelet transform can reduce the duration of the mosaic process and reduce irrelevant image matching points [18] and the training time of the classifier [19]. The image that ECW has compressed with the right configuration can maintain visual quality even though the image size has been reduced.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Lossy Compressed Mosaic for SPOT-6/7 Remote Sensing Data in SPACeMAP

Payani

Wahyuningsih

Yudha

et al. 2021

JOIV : Int. J. Inform. Visualization

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SPACeMAP is a remote-sensing data portal system owned by LAPAN used to distribute mosaic data of Medium-Resolution to Very-High-Resolution for Provincial Governments. The frequently arising problem is that mosaic images have very large data size, especially for SPOT-6/7 mosaic images. The increasing number of data and users may affect the data loading process on the portal so that mosaic data compression can be considered. SPACeMAP has the Image Compressor feature using the Tile and Line algorithms with a compression ratio (target rate) recommended for optics (15 to 20). This study aims to determine the best algorithm and target rate to get compressed mosaic SPOT-6/7 imagery. The comparison method was done qualitatively through visual comparison and quantitatively by using Compression Ratio (CR), Bit per Pixel (BPP), and Peak Signal to Noise Ratio (PSNR). Results of the experiment show that, quantitatively, both Tile and Line algorithms give a different performance, depends on the zoom level and land cover characteristics. In terms of the qualitative result, the Tile algorithm gives better overall results compare to the Line algorithm. Quantitatively, both algorithms show good performance in the homogenous area. The target rate difference on the testing range does not affect process duration, nevertheless, the Line algorithm has a long process duration compare to the Tile algorithm. However, compression mosaics with lower or higher resolution remote sensing data may provide different results. Hence, this need be addressed on further studies.

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A study on fast SIFT image mosaic algorithm based on compressed sensing and wavelet transform

Cited by 25 publications

References 17 publications

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Robust Global Motion Estimation for Video Stabilization Based on Improved K-Means Clustering and Superpixel

Evaluation of Lossy Compressed Mosaic for SPOT-6/7 Remote Sensing Data in SPACeMAP

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