Contrast-based image fusion using the discrete wavelet transform

Tian, Pu; Ni, Guoqiang

doi:10.1117/1.1303728

Cited by 140 publications

(75 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Multi-sensor registration is also affected by the differences in the sensor images. However, image fusion does not necessarily imply multi-sensor sources, there are interesting applications for both single-sensor and multi-sensor image fusion system [7][8].…”

Section: Fig 1 Image Fusionmentioning

confidence: 99%

Multiresolution SVD based Image Fusion

Kumar¹

2017

IOSR JVSP

View full text Add to dashboard Cite

Section: Fig 1 Image Fusionmentioning

confidence: 99%

Multiresolution SVD based Image Fusion

Kumar¹

2017

IOSR JVSP

View full text Add to dashboard Cite

“…So it is apt to fuse satellite remote sensor images. Pu et al [6] suggested a contrast based image fusion method used in the wavelet domain. After decomposition, directive contrast is computed for all decomposed images to fuse them.…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Fusion Technique using Integration of IHS Transform and Contrast based Wavelet Packets

Dheepa¹,

Sukumaran²

2014

IJCA

View full text Add to dashboard Cite

The development of new imaging methods in various fields arise the need of meaningful combination of all available image datasets. Image fusion is the process of integrating complementary information from multiple image sensor data to create a fused image output. The new image generated should contain a more accurate description of the scene than any of the individual source images and is more suitable for human visual and machine perception or further image processing and analysis tasks. This technique is used in satellite remote sensor images to fuse high resolution panchromatic (PAN) image with the low resolution multispectral (MS) image to form a single high resolution multispectral image. Among the existing fusion techniques, wavelet based methods have proved to produce improved results. This paper proposes a novel method to fuse PAN image and MS image by integrating Contrast based Discrete Wavelet Packet Transform (DWPT) and Intensity Hue Saturation (IHS) technique. Firstly, the advantages of using DWPT over DWT are given in brief. Then the proposed algorithm is explained. Finally, its performance is evaluated using various quality assessment metrics which shows that the proposed method is superior to the other existing methods.

show abstract

“…The techniques based on Principal Component Analysis (PCA), Intensity-Hue-Saturation (IHS), Independent Component Analysis (ICA), and Brovey transform (BT) produce spectral degradation [14]. The pyramidal methods such as Gradient pyramid (GP) [15], Laplacian pyramid (LP) [16], Ratio-of-low-pass pyramid (ROLP) [17], Contrast-pyramid (CP) [18], Morphological pyramid [19] suffer from blocking effects. Therefore, these are not highly appropriate methods for medical image fusion [20].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Gomathi¹,

Kalaavathi²

2016

View full text Add to dashboard Cite

Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, Q AB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% -40%, mean is 3% -42%, standard deviation is 1% -42%, Q AB/F is 0.4% -48% in proposed method comparing to conventional methods for six pairs of medical images.

show abstract

Contrast-based image fusion using the discrete wavelet transform

Cited by 140 publications

References 10 publications

Multiresolution SVD based Image Fusion

Multiresolution SVD based Image Fusion

Satellite Image Fusion Technique using Integration of IHS Transform and Contrast based Wavelet Packets

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Contact Info

Product

Resources

About