Blind multichannel reconstruction of high-resolution images using wavelet fusion

An Infrared and Visible Fusion Framework Based on a Novel Decomposition Method

Image fusion is one of the most rapidly evolving fields in image processing today, and its applications are widely expanded in various fields. In the field of image fusion, the method based on multi-scale decomposition plays an important role. However, it faces many difficult puzzles, such as the risk of over-smoothing during decomposition, blurring of fusion results, and loss of details. Aiming at these problems, this paper proposes a novel decomposition-based image fusion framework, which overcomes the problems of noise, blurring, and loss of details. Both the symmetry and asymmetry between infrared and visible images are important research hotspots in this paper. The experiments confirmed that the fusion framework outperforms other methods in both subjective observation and objective evaluation.

Infrared and visible image fusion based on optimal segmenting and contour extraction

Image fusion is the combining process of complementary information of multiple same scene images into an output image. The resultant output image that is named fused image, produces more precise description of the scene than any of the individual input images. In this paper, we propose a novel simple and fast strategy for infrared (IR) and visible images based on local important areas of IR image. The fusion method is completed in three step approach. Firstly, only the segmented regions in the infrared image is extracted. Next, the image fusion is applied on segmented area and finally, contour lines are also used to improve the quality of the results of the second step of fusion method. Using a publicly available database, the proposed method is evaluated and compared to the other fusion methods. The experimental results show the effectiveness of the proposed method compared to the state of the art methods.

An Infrared Defect Sizing Method Based on Enhanced Phase Images

Infrared thermography (IRT) is a full-field, contactless technique that has been widely used for nondestructive evaluation of structural materials due to many advantages. One of the major limitations of IRT is the fuzzy edge and low contrast in the inspected images—as well as the cost of the system. An efficient image post-processing with an affordable and portable device is of great interest to the engineering society. In this study, a convenient and economical inspection system using common halogen lamps was constructed. The corresponding image-processing scheme, which includes Fourier phase analysis and specific image enhancement was developed to identify defects with sharp and clear edges and good contrast. This system was applied to localized of defects in glass-fiber-reinforced composite panels. The results showed that defects with an effective diameter as small as 5 mm can be detected with excellent image quality. As a conclusion, the developed system provides an economic alternative to traditional infrared thermography which is able to identify defects with good qualities.

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

Combing the useful information of multisensor or multifocus images is important for producing effective optical images. To extract and combine the image features of the original images for image fusion well, an algorithm through feature extraction by using the sequentially combined toggle and top-hat based contrast operator is proposed in this paper. Sequentially combining toggle contrast operator and top-hat based contrast operator could be used to identify well the effective bright and dark image features. Furthermore, through multiscale extension, the effective bright and dark image features at multiscales of an image are extracted. After the final bright and dark fusion features are constructed by using the pixel-wise maximum operation on the multiscale image features from different images, the final fusion result is obtained by importing the final bright and dark fusion features into the base image. Experimental results on different types of images show that the proposed algorithm performs well for image fusion, which may be widely used in different applications, such as security surveillance, object recognition, and so on.