Publications by authors named "Mingyao Zheng"
Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map.
View Article and Find Full Text PDFA Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure.
Entropy (Basel)
December 2018
Article Synopsis
- Multi-exposure image fusion methods enhance low-dynamic images taken from the same scene at various exposure levels, resulting in images that better resemble human visual perception.
- This study introduces a new multi-exposure image fusion method that improves local contrast and captures detailed information by using advanced techniques like cartoon-texture decomposition and structural similarity index.
- Experimental results demonstrate that this approach produces high-quality high-dynamic-range images with superior visual effects and detail compared to existing methods.
Multi-modality image fusion provides more comprehensive and sophisticated information in modern medical diagnosis, remote sensing, video surveillance, etc. Traditional multi-scale transform (MST) based image fusion solutions have difficulties in the selection of decomposition level, and the contrast loss in fused image. At the same time, traditional sparse-representation based image fusion methods suffer the weak representation ability of fixed dictionary.
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