15,478 research outputs found
A New Robust Multi focus image fusion Method
In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique
that enables high-resolution imaging of samples, experiences defocused image
quality caused by light scattering when photons propagate through thick
tissues. To circumvent this issue, dualview imaging is helpful. It allows
various sections of the specimen to be scanned ideally by viewing the sample
from opposing orientations. Recent image fusion approaches can then be applied
to determine in-focus pixels by comparing image qualities of two views locally
and thus yield spatially inconsistent focus measures due to their limited
field-of-view. Here, we propose BigFUSE, a global context-aware image fuser
that stabilizes image fusion in LSFM by considering the global impact of photon
propagation in the specimen while determining focus-defocus based on local
image qualities. Inspired by the image formation prior in dual-view LSFM, image
fusion is considered as estimating a focus-defocus boundary using Bayes
Theorem, where (i) the effect of light scattering onto focus measures is
included within Likelihood; and (ii) the spatial consistency regarding
focus-defocus is imposed in Prior. The expectation-maximum algorithm is then
adopted to estimate the focus-defocus boundary. Competitive experimental
results show that BigFUSE is the first dual-view LSFM fuser that is able to
exclude structured artifacts when fusing information, highlighting its
abilities of automatic image fusion.Comment: paper in MICCAI 202
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