Image fusion through feature extraction by using sequentially combined toggle and top-hat based contrast operator

Embedded parallel Fourier ptychographic microscopy reconstruction system

Fourier ptychographic microscopy (FPM) has attracted a wide range of focus for its ability of large space-bandwidth product and quantitative phase imaging. It is a typical computational imaging technique that jointly optimizes imaging hardware and reconstruction algorithms. The data redundancy and inverse problem algorithms are the sources of FPM's excellent performance. But at the same time, this large amount of data processing and complex algorithms also evidently reduce the imaging speed. To accelerate the FPM reconstruction speed, we proposed a fast FPM reconstruction framework consisting of three levels of parallel computation and implemented it with an embedded computing module. In the conventional FPM framework, the sample image is divided into multiple sub-regions to process separately because the illumination angles and defocus distances for different sub-regions may also be different. Our parallel framework first performs digital refocusing and high-resolution reconstruction for each sub-region separately and then stitches the complex sub-regions together to obtain the final high-resolution complex image. The feasibility of the proposed parallel FPM reconstruction framework is verified with different experimental results acquired with the system we built.

Image decomposition fusion method based on sparse representation and neural network

For noisy images, in most existing sparse representation-based models, fusion and denoising proceed simultaneously using the coefficients of a universal dictionary. This paper proposes an image fusion method based on a cartoon + texture dictionary pair combined with a deep neural network combination (DNNC). In our model, denoising and fusion are carried out alternately. The proposed method is divided into three main steps: denoising + fusion + network denoising. More specifically, (1) denoise the source images using external/internal methods separately; (2) fuse these preliminary denoised results with external/internal cartoon and texture dictionary pair to obtain the external cartoon + texture sparse representation result (E-CTSR) and internal cartoon + texture sparse representation result (I-CTSR); and (3) combine E-CTSR and I-CTSR using DNNC (EI-CTSR) to obtain the final result. Experimental results demonstrate that EI-CTSR outperforms not only the stand-alone E-CTSR and I-CTSR methods but also state-of-the-art methods such as sparse representation (SR) and adaptive sparse representation (ASR) for isomorphic images, and E-CTSR outperforms SR and ASR for heterogeneous multi-mode images.

Image fusion via nonlocal sparse K-SVD dictionary learning

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Measure of microscopy mineral image clarity constructed using the sequentially combined toggle and top-hat based contrast operator

Correctly and effectively quantifying the clarity of microscopy mineral image is important and useful to produce clear image for mineral analysis. To well quantify the clarity of mineral image, an effective measure constructed using the sequentially combined toggle and top-hat based contrast operator is proposed in this article. First, the sequentially combined toggle and top-hat based contrast operator is used to extract the effective features which represent the clarity of mineral image. Second, the multiscale clarity features corresponding to each scale are extracted. Finally, after combining the multiscale clarity features in one feature image, the mean value of the feature image is constructed as the measure of mineral image clarity. Because the sequentially combined toggle and top-hat based contrast operator could effectively extract the clarity features, the clarity features are directly used as the measure of image clarity. Therefore, the proposed measure is effective and reasonable. The experimental results on different types of microscopy mineral images verified the effective performance of the proposed measure.