A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion

Abstract Multi-focus image fusion combines two or more images which have different focus values of the same scene using fusion rules. The meaningful image is named all-in-focus image which is more informative and useful for visual perception. In this paper, a novel approach for multi-focus image fusion is proposed. The method is a hybrid method with super-resolution. Firstly, super-resolution method is applied to all source images to enhance information like contrast. Thus, low-resolution source images are converted to high-resolution source images. Secondly, due to decomposing these source images, Stationary Wavelet Transform (SWT) is implemented and images are divided into four sub-bands. These sub-bands are LL (low–low), LH (low–high), HL (high–low) and HH (high–high). LL is the approximation coefficient of source images and others are the detail coefficients of source images. For all these sub-bands, Principal Component Analysis (PCA) is implemented and maximum eigenvector of each sub-band of source images is selected separately to fuse images. Then, Inverse Stationary Wavelet Transform (ISWT) is used to reconstruct the fused sub-bands. Finally, to measure quality of the proposed method objectively, fused image is resized to original source image's size using interpolation based resizing method. To measure the success of method, different metrics without reference image and with reference image, are selected. Results show that the proposed method produce clear edges, good visual perception, good clarity and very few distortion. The proposed hybrid method is applied to produce better quality fused images. Results prove success of the approach in this area. Also visual and quantitative results are very impressive.