9. Classification of various image fusion algorithms and their performance evaluation metrics
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Simrandeep Singh
Abstract
Image fusion is the process of enhancing the perception of a vision by combining substantial information captured by different sensors, different exposure values, and at different focus points. Several images captured from different sensors like infrared region and visible region, positron emission tomography scan, and computed tomography, Multifocus images with different focal points, and images taken by static camera at different exposure values. Most promising area of image processing nowadays is image fusion. The picture fusion method seeks to incorporate two or more pictures into one picture that contains better data than each source picture without adding any artifacts. In distinct apps, it plays an essential role, namely medical diagnostics, pattern detection and identification, navigation, army, civilian surveillance, robotics, and remote sensing satellite images. Three elements are taken into consideration in this review document: spatial domain fusion methodology, different transformation domain techniques, and image fusion performance metrics like entropy, mean, standard deviation, average gradient, peak signal-to-noise ratio, and structural similarity index (SSIM). Many image fusion applications are explored in this chapter.
Abstract
Image fusion is the process of enhancing the perception of a vision by combining substantial information captured by different sensors, different exposure values, and at different focus points. Several images captured from different sensors like infrared region and visible region, positron emission tomography scan, and computed tomography, Multifocus images with different focal points, and images taken by static camera at different exposure values. Most promising area of image processing nowadays is image fusion. The picture fusion method seeks to incorporate two or more pictures into one picture that contains better data than each source picture without adding any artifacts. In distinct apps, it plays an essential role, namely medical diagnostics, pattern detection and identification, navigation, army, civilian surveillance, robotics, and remote sensing satellite images. Three elements are taken into consideration in this review document: spatial domain fusion methodology, different transformation domain techniques, and image fusion performance metrics like entropy, mean, standard deviation, average gradient, peak signal-to-noise ratio, and structural similarity index (SSIM). Many image fusion applications are explored in this chapter.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
