Multi-focus image fusion based on nonsubsampled contourlet transform and residual removal

De-Fencing and Multi-Focus Fusion Using Markov Random Field and Image Inpainting

“…Restoration homographic matrices, dictionary based on k-means labeling, exemplar based inpainting manual initialization Deep learning based methods [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] De-fencing adversarial, structural [21], [22], [23], [24], [25] Fusion multi-scale decomposition, dictionary-learning, nuclear norm regularizer, morphologies constraints, adaptive fusion rules, fractional differential coefficients, geometric sparse coefficients overcomplete dictionary, patch based clustering, single dictionary learning time efficiency, separate fusion and noise removal tasks, information loss due to channel-wise processing Model based methods [26], [27], [28], [29], [30], [31], [32], [33] [34], [35], [36], [37] Fusion SSIM, encoder features, K-means clustering, NSCT, Coupled-Neural-Ps consistency verification photo realistic fusion, blocking artifacts, post processing complete contours; and generator-discriminator setting for prediction of contour completion. In [3], subtraction based on gray-scale binarization is applied on multi-focus auxiliary images to obtain initial mask for image inpainting.…”

“…The spatial domain method seamlessly fuses images from different modalities. Recently, Li et al [30], proposed residual removal based multifocus fusion using multiscale focus correction, and non-subsampled contourlet transform (NSCT). The hybrid framework based on structure tensor shows promising results, and is time efficient.…”

“…In nutshell, deep learning based methods provide better accuracy, however, they have complex architecture and require large training data as compared to classical methods. (d) GFDF [28] (e) CNN [34] (f) MISF [29] (g) NSCT-RR [30] (h) Proposed Since the focused regions contain more high frequency components as compared to de-focused areas, therefore, high frequency components of normalized intensity images are computed. Let A g and B g depict the normalized intensity information contained in source images, defined as,…”

“…This (d) GFDF [28] (e) CNN [34] (f) MISF [29] (g) NSCT-RR [30] (h) Proposed where h r represents the disk operator with radius r ∈ (1, 2), that makes it more suitable to extract high frequency components in the fence region.…”

“…The limits pertaining to fence region components, n 1 and n 2 , are derived from intensity histogram; while the smoothed version of difference image is defined as, (d) GFDF [28] (e) CNN [34] (f) MISF [29] (g) NSCT-RR [30] (h) Proposed…”