Multiexposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range. This is achieved by rendering a single scene based on multiple images captured at different exposure times. Similarly, multifocus image fusion is used when the limited depth of focus on a selected focus setting of a camera results in parts of an image being out of focus. The solution adopted is to fuse together a number of multifocus images to create an image that is focused throughout. A single algorithm that can perform both multifocus and multiexposure image fusion is proposed. This algorithm is a new approach in which a set of unregistered multiexposure/focus images is first registered before being fused to compensate for the possible presence of camera shake. The registration of images is done via identifying matching key-points in constituent images using scale invariant feature transforms. The random sample consensus algorithm is used to identify inliers of SIFT key-points removing outliers that can cause errors in the registration process. Finally, the coherent point drift algorithm is used to register the images, preparing them to be fused in the subsequent fusion stage. For the fusion of images, a new approach based on an improved version of a wavelet-based contourlet transform is used. The experimental results and the detailed analysis presented prove that the proposed algorithm is capable of producing high-dynamic range (HDR) or multifocus images by registering and fusing a set of multiexposure or multifocus images taken in the presence of camera shake. Further, comparison of the performance of the proposed algorithm with a number of state-of-the art algorithms and commercial software packages is provided. In particular, our literature review has revealed that this is one of the first attempts where the compensation of camera shake, a very likely practical problem that can result in HDR image capture using handheld devices, has been addressed as a part of a multifocus and multiexposure image enhancement system.
Multi-exposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of
limited dynamic range. This is achieved by rendering a single scene based on multiple images captured at different
exposure times. Similarly, multi-focus image fusion is used when the limited depth of focus on a selected focus setting of
a camera results in parts of an image being out of focus. The solution adopted is to fuse together a number of multi-focus
images to create an image that is focused throughout. In this paper we propose a single algorithm that can perform both
multi-focus and multi-exposure image fusion. This algorithm is a novel approach in which a set of unregistered multiexposure/
focus images is first registered before being fused. The registration of images is done via identifying matching
key points in constituent images using Scale Invariant Feature Transforms (SIFT). The RANdom SAmple Consensus
(RANSAC) algorithm is used to identify inliers of SIFT key points removing outliers that can cause errors in the
registration process. Finally we use the Coherent Point Drift algorithm to register the images, preparing them to be fused
in the subsequent fusion stage. For the fusion of images, a novel approach based on an improved version of a Wavelet
Based Contourlet Transform (WBCT) is used. The experimental results as follows prove that the proposed algorithm is
capable of producing HDR, or multi-focus images by registering and fusing a set of multi-exposure or multi-focus
images taken in the presence of camera shake.
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