Multi Focus Image Fusion using Combined Median and Average Filter based Hybrid Stationary Wavelet Transform and Principal Component Analysis

• Published in: International journal of advanced computer science & applications Vol. 9; no. 6
• Main Authors: Lianfang, Tian, Ahmed, Jameel, Qiliang, Du, Shankar, Bhawani, Adnan, Saifullah
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2018
• Subjects: Algorithms; Blurring; Cameras; Circuits; Computer vision; Cross correlation; Discrete Wavelet Transform; Illumination; Image contrast; Image enhancement; Image processing; Image quality; Median (statistics); Principal components analysis; Sampling; Satellite imagery; Signal to noise ratio; Spectral methods; Uncertainty principles; Wavelet transforms
• DOI: 10.14569/IJACSA.2018.090605
• ISSN: 2158-107X

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.