Abstract
Transform image processing methods are methods that work in domains of image transforms, such as discrete fourier, discrete cosine, wavelet and alike. They are the basic tools in image compression, image restoration, image resampling and geometrical transformations and can be traced back to the early 1970s. The paper presents a review of these methods with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive transform domain filters for image restoration, to methods of precise image resampling and image reconstruction from sparse samples and up to the ‘compressive sensing’ approach that has gained popularity in the last few years. The review has a tutorial character and purpose.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
