Pixel-level image fusion, which is widely used in remote sensing, medical imaging, surveillance and etc., directly combines the original information in the source images. As a pixel-level method, multi-focus image fusion is designed to combine the partially focused images into one fully fused single image, which is expected to be more informative for human or machine perception. To achieve this purpose, an algorithm using spatial frequency (SF) measure and discrete wavelet transform (DWT) for multi-focus image fusion is proposed. In this work, the source images are decomposed into low frequency components and high frequency components by using DWT. Then the spatial frequency of the low frequency components is calculated. The spatial frequency is used to judge the focused regions, followed by the morphological filter and median filter. The fused low frequency can be obtained. And the high frequency components are fused using traditional method. Finally, the fused image is obtained by doing inverse discrete wavelet transform. To do the comparison, the proposed algorithm is compared with several existing fusion algorithms in qualitative and quantitative ways. Experimental results demonstrate that our method can be competitive or even outperforms the methods in comparison.
Traditional histogram equalization method always leads to the gray level reduction and loss of details. In this paper, an efficient and self-adaptive image enhancement algorithm is proposed based on canny operator and histogram equalization. The canny operator is used to extract the detail information which could be preserved in the enhanced image. The shortcomings of histogram equalization can thus be overcome. The experimental results with infrared images show that our method can preserve more image details and improve the image contrast and suppress noise effectively, which indicates a better performance for infrared image enhancement.