# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work
### Hybrid Image Fusion Models
To show the performance of the proposed technique, a set of test data containing both phenological and land cover type changes in Beijing suburbs, China, are used. [PERSON] et al. [19] have suggested an effective hybrid image fusion approach based on the integer lifting wavelet transform (ILWT) and the discrete cosine transformer (DCT) that are suited for video streaming networks (VSNs). There are two phases in the proposed fusion algorithm. To begin, the ILWT approximation coefficients(low frequencies) are fused by using the variance as an activity level measure in the DCT domain. Second, the high-frequency detail coefficients are fused using the best-weighted average based on the correlation between coefficients in the ILWT domain. The suggested solution addresses information loss, computational complexity, time and energy consumption, and memory space due to integer operations in the ILWT domain. Extensive tests have been carried out to show that the suggested method outperforms other picture fusion algorithms in the literature, both intuitively and numerically. [PERSON] et al. [20] have proposed a multi-focus image fusion benchmark (MFIFB), that includes a test set of 105 picture pairings, a code library of 30 MFIF algorithms, and 20 evaluation measures. MFIFB is the first MFIF benchmark, providing a forum for the community to assess MFIF algorithms thoroughly. To understand the performance of these algorithms further, extensive tests have been carried out utilizing the suggested MFIFB. Effective MFIF algorithms are found by examining the experimental findings. More significantly, some remarks on the current state of the MFIF field are provided, which might aid in a better understanding of this topic. [PERSON] and [PERSON] [21] have suggested an innovative mechanism to obtain multispectral image data using UAVs and fuse them to improve the accuracy of the data. The photos from Parrot's tiny multispectral (MS) camera Sequoia+ are examined at two ancient sites: a Byzantine wall (ground application) in Thessaloniki, Greece, and a mosaic floor (aerial application) at the archaeological site of Dion, Greece. The camera obtains RGB and MS pictures at the same time, which prevents image fusion, as is the case with the conventional use of panchromatic (PAN) and MS images in satellite passive systems. Using the image fusion methods of satellite PAN and MS pictures, this research shows that effective digital processing of the images (RGB and MS) of tiny MS cameras may result in a fused image with a high spatial resolution that maintains a considerable proportion of the original MS image's spectral information. The great spectrum fidelity of the fused pictures allows for high-precision digital measurements in ancient sites, such as precise digital item separation, area measurements, and recovery of information not apparent with standard RGB sensors using MS and RGB data from tiny MS sensors. Reference [22] have proposed a versatile hybrid fusion model to fuse infrared and visible image fusion models. They have used the combined concepts of visibility enhancement and multiscale decomposition to fuse the images. Initially, the authors proposed an effective preprocessing model followed by a decomposition model to decompose the information to the layers of their customized CNN model. Further, they have integrated the concepts of a visual saliency illumination map (VSIM) to retain the contrast information and enhance the fusion process. Reference [12] have proposed a hybrid image fusion model to fuse medical images that exhibit multimodal characteristics. They have used the dual combination of nonsubsampled contourlet transform (NST) and discrete wavelet transform (DTCWT) approaches to fuse the images. The authors have used an advanced CNN model to create weight maps to monitor the pixel movement of the images. Further, the authors have included an advanced comparison-based method to convert the fusion mode to the appropriate coefficients required for the CNN model.