An image fusion method based on content cognition

Ma, Junyong; Zhang, Duo

doi:10.1016/j.procs.2018.04.201

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Image fusion methods can be divided into single-scale and multi-scale / multi-resolution methods [4]. Pyramid image analysis is the first method introduced in this field and includes reduction pyramid filter, gradient pyramid, Laplacian pyramid, ratio pyramid morphological pyramid, and contrast pyramid [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Medical images fusion using two-stage combined model DWT and DCT

Amiri

Rahmanian

Amiri

et al. 2021

International Advanced Researches and Engineering Journal

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The purpose of image enhancement is to improve the interpretation or perception of information in the image for viewers and the input of automated processing systems. Combining a multicenter image is a way of combining several images on a screen, focusing on different objects so that all objects appear in focus in the final image. Wavelet transform and cosine transform are used in many image processing applications, including image fusion. This paper's technique will combine DWT and DCT in two steps for medical MRI and PET images, eventually extracting the combined image. Input images are first divided into 8-pixel blocks in which DCT coefficients are extracted. After extracting the DCT coefficients, the first step of the combination takes place. The re-images will then be combined with the DWT conversion. According to the presented data, the proposed method achieved up to 5% better combination and, as a result, better image quality than the singlestage DWT and DCT methods.

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Section: Introductionmentioning

confidence: 99%

“…Combined models such as Xiao and et al [8] worked on a multi-axis focused image based on edges and focused area extraction and achieved good results. El-Hosny and et al [9] worked on an efficient DT-CWT medical image, and Ma and et al [10] worked on an image composition method based on content recognition and achieved good results.…”

Section: Introductionmentioning

confidence: 99%