Multi-exposure image fusion method based on high-order singular value decomposition

Abstract

The invention discloses a multi-exposure image fusion method based on high-order singular value decomposition. The multi-exposure image fusion method includes the steps: dividing a brightness channelimage of an exposure image into overlapped brightness blocks; obtaining a kernel tensor and a third mode factor matrix of the brightness blocks by utilizing high-order singular value decomposition; obtaining a characteristic coefficient and an activity level measure of the brightness blocks, obtaining a fused brightness block according to the first mode factor matrix and the second mode factor matrix of the brightness block and the characteristic coefficient and the activity level measure, and performing linear transformation on the obtained brightness channel image to obtain a fused brightness channel image; obtaining a fused first chroma channel image by calculating a fusion coefficient of pixel points in the first chroma channel image of the exposure image; obtaining a fused second chroma channel image by calculating a fusion coefficient of pixel points in a second chroma channel image of the exposure image; and obtaining a fused image according to the fused images of the three channels. The multi-exposure image fusion method has the advantage that better detailed textures and rich color information can be obtained.

Description

Technical field [0001] The invention relates to an image fusion technology, in particular to a multi-exposure image fusion method based on high-order singular value decomposition. Background technique [0002] The process of combining information from two or more images of the same scene into a more informative image is called image fusion. Multi-Exposure Image Fusion (MEF) is one of the classic applications of image fusion. Due to the limitation of the dynamic range of a digital camera, images of natural scenes usually have a larger dynamic range than images taken by a digital camera. High Dynamic Range (HDR) imaging technology estimates the Camera Response Function (CRF) based on multiple Low Dynamic Range (LDR) images, and then uses the inverse operation of the camera response function to reconstruct the high Dynamic range image. Since most standard monitors currently in use are low dynamic range, after acquiring high dynamic range images, a tone mapping process is required...

AI Technical Summary

Problems solved by technology

In A. Goshtasby, "Fusion of multi-exposure images," Image and Vision Computing, vol.23, pp.611–618, 2005. (multi-exposure image fusion) using a block-level fusion method, the image is divided into uniform block and use the minimum average method to fuse the best image block, but the contrast and saturation of the image fused by this method are poor

In B.Gu, W.Li, J.Wong, M.Zhu, and M.Wang, “Gradient field multi-exposure images fusion for high dynamic rangeimage visualization,” J.Vis.Commun.Imag.Represent., vol.23 , no.4, pp.604–610, May2012. (Gradient field multi-exposure image fusion for high dynamic range image visualization) proposed an iterative method to modify the gradient field, using twice average filtering and multi-scale nonlinearity Compression method, which obtains the result by solving the Poisson equation and then linearly stretching to the common range, but this method is prone to artifacts

In S.Raman and S.Chaudhuri, "Bilateral filter based compositing for variable exposure photography," inProc.Eurographics, 2009, pp.1–4. (Bilateral filter based compositing for variable exposure photography) proposed a An effective scene synthesis method using an edge-preserving filter, such as a bilateral filter, since there is no restriction on the global brightness consistency, the color of the image fused by this method is prone to distortion, and the overall tone of the image is dark

In K.Ma, H.Li, Z.Wang and D.Meng, “Robust Multi-Exposure Image Fusion: A Structural PatchDecomposition Approach,” IEEE Trans.Image Process., vol.26, no.5, pp.2519- 2532, May 2017. (Multi-exposure image fusion: a structural block decomposition method) proposed to fuse multi-exposure images using structural block decomposition, but this method is not easy to obtain texture information, and the effect of removing artifacts cannot satisfactory

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