Research On Algorithm Of High-dynamic Image Fusion And Gray Characterization Based On Variable X-ray Energy

Without changing the hardware conditions, we can detect the complex structure byvariable X-ray energy imaging technology. Select effective region extraction from imagesacquired under different X-ray energies, after image fusion, and then we can obtain a fusionimage that contains whole structural information. Currently, the existing image fusion methodis established based on that the X-ray is an ideal model of a single energy, but because thecharacteristic of real X-ray is multi-spectrum, so it limit the practical application of thetechnology. So, we proposed an X-ray image fusion method and a gray characterizationmethod in this paper, the results emphasize on the gray image corresponding to characterizeproperties.First of all, because the gray curve is linear in a small area theoretically, so we analyzedand derived the gray relationship between low dynamic image and high dynamic image. Andthen select the best imaging area from the best X-ray energy; thereby the problem of solvinghigh dynamic range fusion image is converted to an optimization problem. And then verifythe feasibility of fusion methods by experiment.Secondly, because the image fusion method was only concerned the informationsuperimposed and ignores relationship between the gray fusion image and the real objectthickness, so we do some research on gray characterization problem of fusion image. Take thefusion image as input, and take the16Bit real high dynamic image as output, and thenestablish the fusion image gray characterization model by neural network method. At thesame time, when the object is different materials, we establish a gray-correctioncharacterization model to realize the gray correct of different materials. Experiment showsthat this method can effectively expand the dynamic range of low dynamic imaging devicefrom the physical sense.