Abstract
Poor illumination, less background contrast and blurring effects makes the medical, satellite and camera images difficult to visualize. Image fusion plays the vital role to enhance image quality by resolving the above issues and reducing the image quantity. The combination of spatial and spectral technique Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) decrease processing time and reduce number of dimensions but down sampling causes lack of shift invariance that results in poor quality final fused image. At first this work uses combined median and average filter that eliminates noise in the image which is caused by illumination, camera circuitry and sensor at preprocessing stage. Then, hybrid Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) technique is implemented to increase output image accuracy by eliminating down sampling and is not influenced by artifacts and blurring effects. Further, it can overcome the trade-off of Heisenberg’s uncertainty principle by improving accuracy in both domains, time (spatial) as well as frequency (spectral). The proposed combined median and average filter with hybrid SWT-PCA algorithm measures quality parameters, such as peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC) and improved results depict the superiority of the algorithm than existing techniques.
Highlights
Image fusion is the process of extracting high quality, more informative single image out of multiple images by removing artifact, noise and blurring effects [1]-[3]
The simulation results given below show the comparison among DWT, SWT, Discrete Wavelet Transform and Principal Component Analysis (DWT-PCA) and combined median and average filter based Stationary Wavelet Transform and Principal Component Analysis (SWT-PCA) and their performance is measured on different quality parameters; peak signal to noise ratio (PSNR), mean squared error (MSE) and normalized cross correlation (NCC)
1) Scenario-01 It can be seen from Fig. 7 that source images with different foci are combined by different image fusion techniques to produce better output image
Summary
Image fusion is the process of extracting high quality, more informative single image out of multiple images by removing artifact, noise and blurring effects [1]-[3]. Spectral domain technique named Curve-let transform is implemented by Choi in [9] It produces better results than discrete wavelet transform but it suffers performance degradation if image is not curved shape. In [11], author has proposed a Discrete Wavelet transform technique that achieves better and precise results with fast computation but it suffers with spatial degradation that reduces the overall performance of output image. In [12], author has implemented the Discrete Stationary Wavelet transform that minimizes spectral degradation in comparison to DWT The shortcoming of this technique is less spatial resolution. The DWT-PCA has been implemented in [13] to achieve better results in both domains, spatial and spectral Though this technique achieves good quality image than existing algorithms, but the final fused image is still affected by shift-invariance due to down sampling.
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