Multifocus Image Fusion Using Biogeography-Based Optimization

For multifocus image fusion in spatial domain, sharper blocks from different source images are selected to fuse a new image. Block size significantly affects the fusion results and a fixed block size is not applicable in various multifocus images. In this paper, a novel multifocus image fusion algorithm using biogeography-based optimization is proposed to obtain the optimal block size. The sharper blocks of each source image are first selected by sum modified Laplacian and morphological filter to contain an initial fused image. Then, the proposed algorithm uses the migration and mutation operation of biogeography-based optimization to search the optimal block size according to the fitness function in respect of spatial frequency. The chaotic search is adopted during iteration to improve optimization precision. The final fused image is constructed based on the optimal block size. Experimental results demonstrate that the proposed algorithm has good quantitative and visual evaluations

A multimodal fusion method for Alzheimer’s disease based on DCT convolutional sparse representation

IntroductionThe medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer’s disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer’ s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed.MethodsThe algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images.Results and discussionThrough extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Non-Standard Imaging Techniques

The first objective of the thesis is to investigate the problem of reconstructing a small-scale object (a few millimeters or smaller) in 3D. In Chapter 3, we show how this problem can be solved effectively by a new multifocus multiview 3D reconstruction procedure which includes a new Fixed-Lens multifocus image capture and a calibrated image registration technique using analytic homography transformation. The experimental results using the real and synthetic images demonstrate the effectiveness of the proposed solutions by showing that both the fixed-lens image capture and multifocus stacking with calibrated image alignment significantly reduce the errors in the camera poses and produce more complete 3D reconstructed models as compared with those by the conventional moving lens image capture and multifocus stacking. The second objective of the thesis is modelling the dual-pixel (DP) camera. In Chapter 4, to understand the potential of the DP sensor for computer vision applications, we study the formation of the DP pair which links the blur and the depth information. A mathematical DP model is proposed which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image . Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularize our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches. Another (third) objective of this thesis is to tackle the multifocus image fusion problem, particularly for long multifocus image sequences. Multifocus image stacking/fusion produces an in-focus image of a scene from a number of partially focused images of that scene in order to extend the depth of field. One of the limitations of the current state of the art multifocus fusion methods is not considering image registration/alignment before fusion. Consequently, fusing unregistered multifocus images produces an in-focus image containing misalignment artefacts. In Chapter 5, we propose image registration by projective transformation before fusion to remove the misalignment artefacts. We also propose a method based on 3D deconvolution to retrieve the in-focus image by formulating the multifocus image fusion problem as a 3D deconvolution problem. The proposed method achieves superior performance compared to the state of the art methods. It is also shown that, the proposed projective transformation for image registration can improve the quality of the fused images. Moreover, we implement a multifocus simulator to generate synthetic multifocus data from any RGB-D dataset. The fourth objective of this thesis is to explore new ways to detect the polarization state of light. To achieve the objective, in Chapter 6, we investigate a new optical filter namely optical rotation filter for detecting the polarization state with a fewer number of images. The proposed method can estimate polarization state using two images, one with the filter and another without. The accuracy of estimating the polarization parameters using the proposed method is almost similar to that of the existing state of the art method. In addition, the feasibility of detecting the polarization state using only one RGB image captured with the optical rotation filter is also demonstrated by estimating the image without the filter from the image with the filter using a generative adversarial network