Jianwen Hu

Image Fusion With Guided Filtering

A fast and effective image fusion method is proposed for creating a highly informative fused image through merging multiple images. The proposed method is based on a two-scale decomposition of an image into a base layer containing large scale variations in intensity, and a detail layer capturing small scale details. A novel guided filtering-based weighted average technique is proposed to make full use of spatial consistency for fusion of the base and detail layers. Experimental results demonstrate that the proposed method can obtain state-of-the-art performance for fusion of multispectral, multifocus, multimodal, and multiexposure images.

Performance comparison of different multi-resolution transforms for image fusion

Image fusion combines information from multiple images of the same scene to get a composite image that is more suitable for human visual perception or further image-processing tasks. In this paper, we compare various multi-resolution decomposition algorithms, especially the latest developed image decomposition methods, such as curvelet and contourlet, for image fusion. The investigations include the effect of decomposition levels and filters on fusion performance. By comparing fusion results, we give the best candidates for multi-focus images, infrared-visible images, and medical images. The experimental results show that the shift-invariant property is of great importance for image fusion. In addition, we also conclude that short filter usually provides better fusion results than long filter, and the appropriate setting for the number of decomposition levels is four.

Pixel-level image fusion

This review provides a survey of various pixel-level image fusion methods according to the adopted transform strategy.The existing fusion performance evaluation methods and the unresolved problems are concluded.The major challenges met in different image fusion applications are analyzed and concluded. Pixel-level image fusion is designed to combine multiple input images into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. Due to this advantage, pixel-level image fusion has shown notable achievements in remote sensing, medical imaging, and night vision applications. In this paper, we first provide a comprehensive survey of the state of the art pixel-level image fusion methods. Then, the existing fusion quality measures are summarized. Next, four major applications, i.e., remote sensing, medical diagnosis, surveillance, photography, and challenges in pixel-level image fusion applications are analyzed. At last, this review concludes that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications. Therefore, the researches in the image fusion field are still expected to significantly grow in the coming years.

Image matting for fusion of multi-focus images in dynamic scenes

In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes.

The multiscale directional bilateral filter and its application to multisensor image fusion

In this paper, a novel multiscale geometrical analysis called the multiscale directional bilateral filter (MDBF) which introduces the nonsubsampled directional filter bank into the multiscale bilateral filter is proposed. Through combining the characteristic of preserving edge of the bilateral filter with the ability of capturing directional information of the directional filter bank, the MDBF can better represent the intrinsic geometrical structure of images. The MDBF, which is a multiscale, multidirectional and shift-invariant image decomposition scheme, is used to fuse multisensor images in this paper. The source images are first decomposed into the directional detail subbands and the approximation subbands via the MDBF. Then, the directional detail subbands and the approximation subbands are fused according to the given fusion rule, respectively. Finally, the inverse MDBF is applied to the fused subbands to obtain the fused image. Experimental results over visible and infrared images and medical images demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures.

Fusion of panchromatic and multispectral images using multiscale dual bilateral filter

This paper presents a novel method based on the developed multiscale dual bilateral filter to fuse high spatial resolution panchromatic image and high spectral resolution multispectral image. Compared with traditional multi-resolution based methods, the process of detail extraction considers the characteristics of panchromatic image and multispectral image simultaneously. The low resolution multispectral image is resampled to the same size of the high resolution panchromatic image and sharpened through injecting the extracted details. The proposed fusion method is tested over QuickBird and IKONOS images and compared with three popular methods. The experimental results demonstrate that our method outperforms conventional methods.

Multitemporal image change detection with compressed sparse representation

In this paper, we propose a novel feature vector clustering method for unsupervised change detection in multitemporal satellite images. A feature vector for each pixel is extracted using the compressed sparse representation of the difference image which is obtained by comparing a pair of co-registered images acquired at different times on the same area. The compressed sparse representation is achieved by taking two stages: compressed sampling and sparse representation. The compressed sampling is first employed in order to reduce the dimensionality of the feature vectors. Then, the sparse representation is applied to extract the meaningful change information and to combat the noise interference. The final change detection is obtained by clustering the extracted feature vectors using k-means algorithm into “changed” and “unchanged” classes. Experimental results clearly show that the proposed approach consistently yields superior performance compared to several well-known change detection techniques on both noise-free and noisy satellite images.

Single image super resolution via texture constrained sparse representation

Image super resolution is a challenging highly ill-posed inverse problem. In this paper, we proposed a texture constrained sparse representation for single image super resolution. Firstly, the low resolution observed image is segmented into different texture regions. Through preprepared texture databases, the low resolution regions are classified into different texture categories using the designed texture classifier. Then, the high resolution segments are reconstructed by sparse representation with relevant texture dictionaries. Integrating all segments, the high resolution result is obtained. The proposed method is compared with sparse representation method and some existing methods. The experimental results show that our method achieves better results in visual inspection and quantitative analysis.