An FPGA implementation of pattern-Selective pyramidal image fusion

The aim of image fusion is to combine multiple images (from one or more sensors) into a single composite image that retains all useful data without introducing artefacts. Pattern-selective techniques attempt to identify and extract whole features in the source images to use in the composite. These techniques usually rely on multiresolution image representations such as Gaussian pyramids, which are localised in both the spatial and spatial-frequency domains, since they enable identification of features at many scales simultaneously. This paper presents an FPGA implementation of pyramidal decomposition and subsequent fusion of dual video streams. This is the first reported instance of a hardware implementation of pattern-selective pyramidal image fusion. Use of FPGA technology has enabled a design that can fuse dual video streams (greyscale VGA, 30fps) in real-time, and provides approximately 100 times speedup over a 2.8GHz Pentium-

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201

Comparative study of Image Fusion Methods: A Review

As the size and cost of sensors decrease, sensor networks are increasingly becoming an attractive method to collect information in a given area. However, one single sensor is not capable of providing all the required information,either because of their design or because of observational constraints. One possible solution to get all the required information about a particular scene or subject is data fusion.. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterization using a fusion evaluation framework based on gradient information representation. We give the framework of the overallnbsp system and explain its USAge method. The system has many functions: image denoising, image enhancement, image registration, image segmentation, image fusion, and fusion evaluation. This paper presents a literature review on some of the image fusion techniques for image fusion like, Laplace transform, Discrete Wavelet transform based fusion, Principal component analysis (PCA) based fusion etc. Comparison of all the techniques can be the better approach fornbsp future research

Multi Focus Image Fusion Techniques

The single image required high spectral information and high quality for human visual perception but sensor or instrument may be not capable to provide our demand. We solved this problem using fusion process. Multi focus image fusion is a process of combining information of two or more images which capture at different direction or different angle of same scene and resultant quality of image is higher than the input image. The main goal of this paper is to implement the various method such as pixel level fusion (simple average, simple minimum, simple maximum), Discrete Wavelet transform based fusion, Principal component analysis (PCA) , Laplacian Pyramid fusion and to determine which method provide better result for human visual perception. DOI: 10.17762/ijritcc2321-8169.16045

Image enhancement using fusions by Wavelet Transform, Laplacian Pyramid and combination of both.

This paper represents idea of combining multiple image modalities to provide a single, enhanced image is well established different fusion methods have been proposed in literature. This paper is based on image fusion using wavelet transform, laplacian pyramid and combination of laplacian pyramid and wavelet transform method. Images of same size are used for experimentation. Images used for the experimentation are standard images and averaging filter is used of equal weights in original images to burl. Performance of image fusion technique is measured by mean square error, normalized absolute error and peak signal to noise ratio. proposed method is compared with wavelet transform method and laplacian pyramid method, from the performance analysis it has been observed that MSE is decreased in case of all three the methods where as PSNR Increased, NAE decreased in case of laplacian pyramid and Combination of laplacian pyramid and wavelet transform where as constant for wavelet transform method

Novel Image Fusion Technique Based On DWT & MSVD

Image fusion is the process of combining two or more images with specific objects with more clarity. It is common that in focusing one object remaining objects will be less highlighted. Hence to get an image highlighted in all regions, a different means is required. This is what is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques where the substitution operations are done pixel value by pixel value. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and then reconstruct back into the time domain. In this paper, new transform techniques like singular value decomposition will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones