Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic
This work addresses image quality enhancement for applications such as medical imaging and remote sensing, but it is incremental as it builds on existing fusion techniques.
The paper tackled the problem of pixel-level image fusion by proposing a fuzzy logic method to combine images from different sensors, resulting in improved quality of the fused image compared to wavelet transform and genetic algorithm-based methods, as measured by parameters like IQI, MIM, RMSE, PSNR, FF, FS, FI, and entropy.
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual information measure (MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported methods, wavelet transform based image fusion and weighted average discrete wavelet transform based image fusion using genetic algorithm.