Fusion of multi-resolution visible image and infrared images based on guided filter

Fan, Zhongpeng; Yan, Liping; Xia, Yuanqing; Fu, Mengyin; Xiao, Bo

doi:10.23919/chicc.2018.8483810

Cited by 3 publications

(1 citation statement)

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“…2) to upgrade the perceivability of dim locale content in the decayed noticeable image, a novel night-vision upgrade technique is introduced dependent on guided channel; 3) the infrared image data is infused into the unmistakable image through a multi-scale fusion approach dependent on guided channel; 4) converse wavelet change is utilized to accomplish the fusion image; 5) to additionally enhance the perceivability of the fusion image, it is improved by utilization of a versatile upgrade method. [2] C. C. Chaithra, N. L. Taranath, Image fusion is the way toward consolidating at least two important data into one image. The came about image will have more logical than unique images.…”

Section: Literature Surveymentioning

confidence: 99%

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

Kumari¹,

Rawat²

2018

IJCA

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Nowadays lots of work is going to be done on the field of image fusion and also used in various application such as medical imaging and multi spectra sensor image fusing etc. For fusing the image various techniques has been proposed by different previous works such as wavelet transform, IHS (Intensity, Hue and Saturation) and Principal Component Analysis (PCA), based methods etc. In this paper literature of the image fusion is discussed with implementation using wavelet transform used for the specific application as in the image restoration field. Using Image fusion may improve the perceptual quality of the restored images. Usually image deblurring methods are used at the front end for restoration and then image fusion is used for improving the visual quality. Paper uses three de-blurring technique to blindly restoring the image then use statistical parameters for adopting the best fused images out of various hybrid fusion results. Performance is tested on images with distinct features.

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Section: Literature Surveymentioning

confidence: 99%