Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input source data. In this paper, we have demonstrated a comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail. The reference-based and non-reference-based image quality evaluation metrics are summarized together with recent trends in image fusion. Several image fusion applications in various fields have also been reported. It has been stated that though a lot of singular fusion techniques seemed to have given optimum results, the focus of researchers is shifting toward amalgamated or hybrid fusion techniques, which could harness the attributes of both multi-scale and non-multi-scale decomposition methods. Toward the end, the review is concluded with various open challenges for researchers. Thus, the descriptive study in this paper would form basis for stimulating and nurturing advanced research ideas in image fusion.

From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications

Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients. Specifically, we first construct a discriminative Fisher atom embedding model by exploring the Fisher criterion of the atoms, which encourages the atoms of the same class to reconstruct the corresponding training samples as much as possible. At the same time, a discriminative Fisher coefficient embedding model is formulated by imposing the Fisher criterion on the profiles (row vectors of the coding coefficient matrix) and coding coefficients, which forces the coding coefficient matrix to become a block-diagonal matrix. Since the profiles can indicate which training samples are represented by the corresponding atoms, the proposed two discriminative Fisher embedding models can alternatively and interactively promote the discriminative capabilities of the learned dictionary and coding coefficients. The extensive experimental results demonstrate that the proposed DFEDL algorithm achieves superior performance in comparison with some state-of-the-art dictionary learning algorithms on both hand-crafted and deep learning-based features.

Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition

The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

/pdf/image-fusion-techniques-a-survey-4bx3psydco.pdf

Image Fusion Techniques: A Survey

The invention discloses a tracking-learning-detection (TLD)-based video object tracking method, which comprises the following steps of: (1) when a start frame of an object is tracked, generating an image sub-window according to given position and size information, and training a detector; (2) estimating the area of the object in a current frame according to the position and size information of the object in a previous frame by using a tracker; (3) detecting the current frame by using the detector, and finding all possible objects in the current frame; and (4) performing fusion processing on results of the tracker and the detector, judging whether the object exists in the current frame or not, returning to the step (3) to process the next frame if the object does not exist in the current frame, judging whether a tracking trajectory of the current frame is effective or not if the object exists in the current frame, giving the position of the object, returning to the step (2) to process the next frame if the tracking trajectory of the current frame is ineffective, finishing the online updating of the detector if the tracking trajectory of the current frame is effective, and returning to the step (2) to process the next frame. According to the method, the conventional TLD algorithm is improved to obtain a video object tracking algorithm which is more ideal than the TLD algorithm.

Tracking-learning-detection (TLD)-based video object tracking method

Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is an emerging imaging technique that holds great promise for biomedical imaging. PACT is a hybrid imaging method that can exploit the strong endogenous contrast of optical methods along with the high spatial resolution of ultrasound methods. In its canonical form that is addressed in this article, PACT seeks to estimate the photoacoustically-induced initial pressure distribution within the object. Image reconstruction methods are employed to solve the acoustic inverse problem associated with the image formation process. When an idealized imaging scenario is considered, analytic solutions to the PACT inverse problem are available; however, in practice, numerous challenges exist that are more readily addressed within an optimization-based, or iterative, image reconstruction framework. In this article, the PACT image reconstruction problem is reviewed within the context of modern optimization-based image reconstruction methodologies. Imaging models that relate the measured photoacoustic wavefields to the sought-after object function are described in their continuous and discrete forms. The basic principles of optimization-based image reconstruction from discrete PACT measurement data are presented, which includes a review of methods for modeling the PACT measurement system response and other important physical factors. Non-conventional formulations of the PACT image reconstruction problem, in which acoustic parameters of the medium are concurrently estimated along with the PACT image, are also introduced and reviewed.

https://iopscience.iop.org/article/10.1088/1361-6560/ab2017/pdf

A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography.

Soft robots refer to robots that are softer and more flexible when compared with conventional rigid-bodied robots. Soft robots are adapted to unstructured environments due to their flexibility, deformability and energy-absorbing properties. Thus, they have tremendous application prospects in on-orbit servicing (OOS). This study discusses the configuration and manipulation of soft robotics. Usually, learning from living beings is used to develop the configurations of most soft robots. In this study, typical soft robots are introduced based on what they mimic. The discussion of manipulation is divided into two parts, namely actuation and control. The study also involves describing and comparing several types of actuations. Studies on the control of soft robots are also reviewed. In this study, potential application of soft robotics for on-orbit servicing is analyzed. A hybrid configuration and manipulation of space soft robots for future research are proposed based on the current development of soft robotics, and some challenges are discussed.

An overview of the configuration and manipulation of soft robotics for on-orbit servicing

A sparse proximal Newton splitting method for constrained image deblurring

Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

Image restoration is a difficult and challenging problem in various imaging applications However, despite of the benefits of a single overcomplete dictionary, there are still several challenges for capturing the geometric structure of image of interest To more accurately represent the local structures of the underlying signals, we propose a new problem formulation for sparse representation with block-orthogonal constraint There are three contributions First, a framework for discriminative structured dictionary learning is proposed, which leads to a smooth manifold structure and quotient search spaces Second, an alternating minimization scheme is proposed after taking both the cost function and the constraints into account This is achieved by iteratively alternating between updating the block structure of the dictionary defined on Grassmann manifold and sparsifying the dictionary atoms automatically Third, Riemannian conjugate gradient is considered to track local subspaces efficiently with a convergence guarantee Extensive experiments on various datasets demonstrate that the proposed method outperforms the state-of-the-art methods on the removal of mixed Gaussian-impulse noise

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration

http://link.springer.com/content/pdf/10.1007/978-3-319-90716-1.pdf

Han Pan

Papers

An overview of the configuration and manipulation of soft robotics for on-orbit servicing

A sparse proximal Newton splitting method for constrained image deblurring

Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration

Non-Cooperative Target Tracking, Fusion and Control