Abstract
Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality.
Highlights
Image fusion is a technique for combining complementary information obtained from different sensors to enhance the visual perception of the human eye or to facilitate the image processing and computer vision
Performance evaluation of the proposed fusion method was achieved using some of no reference operators, which are image Contrast (CON), Gaussian derivative, (GD), Gradient energy (GE), and Variance of wavelet coefficients (VoWAV)
Figure- 4 shows the fused images that are obtained using image fusion based on Discrete wavelet transform (DWT), DT-CWT, and the proposed fusion method with fusion rule based on maximum section
Summary
Image fusion is a technique for combining complementary information obtained from different sensors to enhance the visual perception of the human eye or to facilitate the image processing and computer vision. Image fusion technology is used in many applications such as medical fields, military, video surveillance, remote sensing, etc. The merging process is carried out either in the frequency domain or in the spatial domain in three levels: pixel, feature, and decision fusion levels. Many fusion methods and techniques have been implemented to improve and develop the image merging process to reach the best results. Various recent surveys outline these methods [2,3,4,5]. Many transforms are used in the fusion field, like Stationary Wavelet Transform, Discrete Wavelet
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