4 research outputs found
Image Fusion With Cosparse Analysis Operator
The paper addresses the image fusion problem, where multiple images captured
with different focus distances are to be combined into a higher quality
all-in-focus image. Most current approaches for image fusion strongly rely on
the unrealistic noise-free assumption used during the image acquisition, and
then yield limited robustness in fusion processing. In our approach, we
formulate the multi-focus image fusion problem in terms of an analysis sparse
model, and simultaneously perform the restoration and fusion of multi-focus
images. Based on this model, we propose an analysis operator learning, and
define a novel fusion function to generate an all-in-focus image. Experimental
evaluations confirm the effectiveness of the proposed fusion approach both
visually and quantitatively, and show that our approach outperforms
state-of-the-art fusion methods.Comment: 12 pages, 4 figures, 1 table, Submitted to IEEE Signal Processing
Letters on December 201
A Fast Dictionary Learning Method for Coupled Feature Space Learning
In this letter, we propose a novel computationally efficient coupled
dictionary learning method that enforces pairwise correlation between the atoms
of dictionaries learned to represent the underlying feature spaces of two
different representations of the same signals, e.g., representations in
different modalities or representations of the same signals measured with
different qualities. The jointly learned correlated feature spaces represented
by coupled dictionaries are used in sparse representation based classification,
recognition and reconstruction tasks. The presented experimental results show
that the proposed coupled dictionary learning method has a significantly lower
computational cost. Moreover, the visual presentation of jointly learned
dictionaries shows that the pairwise correlations between the corresponding
atoms are ensured.Comment: 12 pages, 3 figures, 1 algorith
Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning
We address the multi-focus image fusion problem, where multiple images
captured with different focal settings are to be fused into an all-in-focus
image of higher quality. Algorithms for this problem necessarily admit the
source image characteristics along with focused and blurred features. However,
most sparsity-based approaches use a single dictionary in focused feature space
to describe multi-focus images, and ignore the representations in blurred
feature space. We propose a multi-focus image fusion approach based on sparse
representation using a coupled dictionary. It exploits the observations that
the patches from a given training set can be sparsely represented by a couple
of overcomplete dictionaries related to the focused and blurred categories of
images and that a sparse approximation based on such coupled dictionary leads
to a more flexible and therefore better fusion strategy than the one based on
just selecting the sparsest representation in the original image estimate. In
addition, to improve the fusion performance, we employ a coupled dictionary
learning approach that enforces pairwise correlation between atoms of
dictionaries learned to represent the focused and blurred feature spaces. We
also discuss the advantages of the fusion approach based on coupled dictionary
learning, and present efficient algorithms for fusion based on coupled
dictionary learning. Extensive experimental comparisons with state-of-the-art
multi-focus image fusion algorithms validate the effectiveness of the proposed
approach.Comment: 25 pages, 15 figures, 2 tabl
The bilateral solver for quality estimation based multi-focus image fusion
In this work, a fast Bilateral Solver for Quality Estimation Based
multi-focus Image Fusion method (BS-QEBIF) is proposed. The all-in-focus image
is generated by pixel-wise summing up the multi-focus source images with their
focus-levels maps as weights. Since the visual quality of an image patch is
highly correlated with its focus level, the focus-level maps are preliminarily
obtained based on visual quality scores, as pre-estimations. However, the
pre-estimations are not ideal. Thus the fast bilateral solver is then adopted
to smooth the pre-estimations, and edges in the multi-focus source images can
be preserved simultaneously. The edge-preserving smoothed results are utilized
as final focus-level maps. Moreover, this work provides a confidence-map
solution for the unstable fusion in the focus-level-changed boundary regions.
Experiments were conducted on pairs of source images. The proposed
BS-QEBIF outperforms the other fusion methods objectively and
subjectively. The all-in-focus image produced by the proposed method can well
maintain the details in the multi-focus source images and does not suffer from
any residual errors. Experimental results show that BS-QEBIF can handle the
focus-level-changed boundary regions without any blocking, ringing and blurring
artifacts