188 research outputs found
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
Multi-focus image fusion, a technique to generate an all-in-focus image from
two or more partially-focused source images, can benefit many computer vision
tasks. However, currently there is no large and realistic dataset to perform
convincing evaluation and comparison of algorithms in multi-focus image fusion.
Moreover, it is difficult to train a deep neural network for multi-focus image
fusion without a suitable dataset. In this letter, we introduce a large and
realistic multi-focus dataset called Real-MFF, which contains 710 pairs of
source images with corresponding ground truth images. The dataset is generated
by light field images, and both the source images and the ground truth images
are realistic. To serve as both a well-established benchmark for existing
multi-focus image fusion algorithms and an appropriate training dataset for
future development of deep-learning-based methods, the dataset contains a
variety of scenes, including buildings, plants, humans, shopping malls, squares
and so on. We also evaluate 10 typical multi-focus algorithms on this dataset
for the purpose of illustration
A Novel Multi-Focus Image Fusion Method Based on Stochastic Coordinate Coding and Local Density Peaks Clustering
abstract: The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods
Construction of all-in-focus images assisted by depth sensing
Multi-focus image fusion is a technique for obtaining an all-in-focus image
in which all objects are in focus to extend the limited depth of field (DoF) of
an imaging system. Different from traditional RGB-based methods, this paper
presents a new multi-focus image fusion method assisted by depth sensing. In
this work, a depth sensor is used together with a color camera to capture
images of a scene. A graph-based segmentation algorithm is used to segment the
depth map from the depth sensor, and the segmented regions are used to guide a
focus algorithm to locate in-focus image blocks from among multi-focus source
images to construct the reference all-in-focus image. Five test scenes and six
evaluation metrics were used to compare the proposed method and representative
state-of-the-art algorithms. Experimental results quantitatively demonstrate
that this method outperforms existing methods in both speed and quality (in
terms of comprehensive fusion metrics). The generated images can potentially be
used as reference all-in-focus images.Comment: 18 pages. This paper has been submitted to Computer Vision and Image
Understandin
A Review on Multi-Focus Image Fusion
Image fusion is a process to collect the information of the images of the same scene from the different images with a focus on different objects. The Multi-focus image performs a vital role in image process and visual applications. The multi-focus image fusion could be a technique seeks to provide an effective activity level measurement to produce the clarity of source images. It finds application in various fields such as remote sensing, optical microscopy, medical diagnostics, forensic and defense departments. This paper presents totally different multi-focus image fusion techniques in spatial and frequency domain
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