Partial differential equations (PDEs) have been applied successfully to formulate some dynamical phenomena in many engineering domains, since these equations model continuous change. Thus, in the last 35 years, PDEs have been used to solve many challenges in various image and video processing and analysis and computer vision areas, including image filtering, inpainting, segmentation, decomposition, compression and registration, and video motion estimation. Variational and non-variational diffusion-based models have been applied successfully in all these fields. Also, variational methods are effective mathematical tools to solve problems of image processing and analysis in combination with machine learning and deep learning approaches.
The main objective of this Research Topic is to disseminate valuable original research in these image and video processing and analysis domains, providing novel PDE-based approaches in these areas and bringing together the achievements of the researchers in these fields. Therefore, we encourage the authors to contribute original and high-quality submissions that present new theoretical and practical results on the following topics, and consider unaddressed aspects of them, as well as survey articles describing the state of the art methods.
• Nonlinear second and fourth-order anisotropic diffusion-based image restoration models
• Image denoising and edge detection techniques using reaction-diffusion equations
• Novel image interpolation methods using variational and non-variational PDE models
• Structural inpainting techniques using fluid equations
• Partial differential equation-based image compression techniques
• Variational level-set based algorithms for image segmentation
• Nonlinear variational PDE schemes for video motion estimation
• Image processing and analysis models using Fokker–Planck equations
• New variational models for image registration and fusion
• Multiscale and multiresolution image analysis techniques using anisotropic diffusion-based scale spaces
• Variational Methods for Machine Learning/Deep Learning with Applications in Image Processing and Analysis.
Partial differential equations (PDEs) have been applied successfully to formulate some dynamical phenomena in many engineering domains, since these equations model continuous change. Thus, in the last 35 years, PDEs have been used to solve many challenges in various image and video processing and analysis and computer vision areas, including image filtering, inpainting, segmentation, decomposition, compression and registration, and video motion estimation. Variational and non-variational diffusion-based models have been applied successfully in all these fields. Also, variational methods are effective mathematical tools to solve problems of image processing and analysis in combination with machine learning and deep learning approaches.
The main objective of this Research Topic is to disseminate valuable original research in these image and video processing and analysis domains, providing novel PDE-based approaches in these areas and bringing together the achievements of the researchers in these fields. Therefore, we encourage the authors to contribute original and high-quality submissions that present new theoretical and practical results on the following topics, and consider unaddressed aspects of them, as well as survey articles describing the state of the art methods.
• Nonlinear second and fourth-order anisotropic diffusion-based image restoration models
• Image denoising and edge detection techniques using reaction-diffusion equations
• Novel image interpolation methods using variational and non-variational PDE models
• Structural inpainting techniques using fluid equations
• Partial differential equation-based image compression techniques
• Variational level-set based algorithms for image segmentation
• Nonlinear variational PDE schemes for video motion estimation
• Image processing and analysis models using Fokker–Planck equations
• New variational models for image registration and fusion
• Multiscale and multiresolution image analysis techniques using anisotropic diffusion-based scale spaces
• Variational Methods for Machine Learning/Deep Learning with Applications in Image Processing and Analysis.
