RADAR Image Fusion Using Wavelet Transform

RADAR Images are strongly preferred for analysis of geospatial information about earth surface to assesse envirmental conditions radar images are captured by different remote sensors and that images are combined together to get complementary information. To collect radar images SAR(Synthetic Aperture Radar) sensors are used which are active sensors and can gather information during day and night without affecting weather conditions. We have discussed DCT and DWT image fusion methods,which gives us more informative fused image simultaneously we have checked performance parameters among these two methods to get superior method from these two techniques

Novel Image Fusion Technique Based On DWT & MSVD

Image fusion is the process of combining two or more images with specific objects with more clarity. It is common that in focusing one object remaining objects will be less highlighted. Hence to get an image highlighted in all regions, a different means is required. This is what is done by the Image Fusion. In remote sensing, the increasing availability of Space borne images and synthetic aperture radar images gives a motivation to different kinds of image fusion algorithms. In the literature a number of time domain image fusion techniques where the substitution operations are done pixel value by pixel value. Few transform domain fusion techniques are proposed. In transform domain fusion techniques, the source images will be decomposed, then integrated into a single data and then reconstruct back into the time domain. In this paper, new transform techniques like singular value decomposition will be utilized for image fusion. In the literature, the quality assessment of fusion techniques is mainly by subjective tests. In this paper, objective quality assessment metrics are calculated for existing and proposed techniques. It has been found that the new image fusion technique outperformed the existing ones

Multi-Modal Medical Image Fusion using Multi-Resolution Discrete Sine Transform

Quick advancement in high innovation and current medical instrumentations, medical imaging has turned into a fundamental part in many applications such as in diagnosis, research and treatment. Images from multimodal imaging devices usually provide complementary and sometime conflicting information. Information from one image may not be adequate to give exact clinical prerequisites to the specialist or doctor. Of-late, Multi-Model medical image fusion playing a challenging role in current research areas. There are many theories and techniques developed to fuse the multimodal images by researchers. In this paper, introducing a new algorithm called as Multi Resolution Discrete Sine Transform which is used for Multi-Model image fusion in medical applications. Performance and evaluation of this algorithm is presented. The main intention of this paper is to apply DST which is easy to understand and demonstrated method to process image fusion techniques. The proposed MDST based image fusion algorithm performance is compared with that of the well-known wavelet based image fusion algorithm. From the results it is observed that the performance of image fusion using MDST is almost similar to that of wavelet based image fusion algorithm. The proposed MDST based image fusion techniques are computationally very simple and it is suitable. The proposed MDST based image fusion algorithms are computationally, exceptionally basic and it is appropriate for real time medical diagnosis applications

Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets.

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach

Bounded PCA based Multi Sensor Image Fusion Employing Curvelet Transform Coefficients

The fusion of thermal and visible images acts as an important device for target detection. The quality of the spectral content of the fused image improves with wavelet-based image fusion. However, compared to PCA-based fusion, most wavelet-based methods provide results with a lower spatial resolution. The outcome gets better when the two approaches are combined, but they may still be refined. Compared to wavelets, the curvelet transforms more accurately depict the edges in the image. Enhancing the edges is a smart way to improve spatial resolution and the edges are crucial for interpreting the images. The fusion technique that utilizes curvelets enables the provision of additional data in both spectral and spatial areas concurrently. In this paper, we employ an amalgamation of Curvelet Transform and a Bounded PCA (CTBPCA) method to fuse thermal and visible images. To evidence the enhanced efficiency of our proposed technique, multiple evaluation metrics and comparisons with existing image merging methods are employed. Our approach outperforms others in both qualitative and quantitative analysis, except for runtime performance. Future Enhancement-The study will be based on using the fused image for target recognition. Future work should also focus on this method’s continued improvement and optimization for real-time video processing