Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain

Abstract

In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images.

Multi-Scale FPGA-Based Infrared Image Enhancement by Using RGF and CLAHE

“…To objectively evaluate the enhancement effects of the different methods in the ten aforementioned infrared scenes, five traditional image evaluation metrics were employed, such as average gradient (AG) [48] and edge intensity (EI) [49], which are based on image features; figure definition (FD), which quantifies the level of detail and distinctness present in the visual content of the image; and root mean square contrast (RMSC) [50], which quantifies the contrast level of the image. These metrics are widely used for evaluating the quality of an image.…”

Multi-Focus Image Fusion via PAPCNN and Fractal Dimension in NSST Domain

“…These comparative algorithms include parameter-adaptive pulse coupled neural network and nonsubsampled shearlet transform (NSSTPA) [3], proportional maintenance of gradient and intensity (PMGI) [28], threelayer decomposition and sparse representation (TLDSR) [35], convolutional simultaneous sparse approximation (CSSA) [36], local extreme map guided multi-modal image fusion (LEGFF) [37], unified unsupervised image fusion network (U2Fusion) [38], distanceweighted regional energy and nonsubsampled shearlet transform (NSSTDW) [18], and zero-shot multi-focus image fusion (ZMFF) [2]. Qualitative and quantitative evaluations were used to evaluate the results of fusion; the quantitative evaluation indicators include the edge-based similarity measurement Q AB/F [39,40], the structural similarity based metric Q E [41], the feature mutual information metric Q FMI [42], the gradient based metric Q G [41], the nonlinear correlation information entropy Q NCIE [41], the phase-congruencybased metric Q P [41], the mutual information metric Q MI [39,40], the normalized mutual information Q N MI [41], the structural-similarity-based metric Q Y introduced by Yang et al Q Y [41,43] and the average gradient metric Q AG [44,45], peak signal-to-noise ratio Q PSNR [46], and mean square error Q MSE [47]. The algorithms with larger indicator values (except for metric Q MSE ) are better.…”