Digital image processing is a popular, rapidly growing area of electrical and computer engineering. Digital image processing has enabled various intelligent applications such as face recognition, signature recognition, iris recognition, forensics, automobile detection, and military vision applications. Its growth is leveraged by technological innovations in the fields of computer processing, digital imaging, and mass storage devices. Traditional analog imaging applications are now switching to digital systems to utilize their usability and affordability. Important examples include photography, medicine, video production, remote sensing, and security monitoring. These sources produce a huge volume of digital image data every day. Theoretically, image processing can be considered as the processing of a two-dimensional image using a digital computer. The outcome of image processing could be an image, a set of features, or characteristics related to the image. Most image processing methods treat an image as a two-dimensional signal and implement standard signal processing techniques. Many image processing techniques were of only academic interest because of their computational complexity. However, recent advances in processing and memory technology made image processing a vital and cost-effective technology in a host of applications. Multi-scale image transformations, such as Fourier transform, wavelet transform, complex wavelet transform, quaternion wavelet transform, ridgelet transform, contourlet transform, curvelet transform, and shearlet transform, play an extremely crucial role in image compression, image denoising, image restoration, image enhancement, and super-resolution. Fourier transform is a powerful tool that has been available to signal and image analysis for many years. However, the problem with using Fourier transform is frequency analysis cannot offer high frequency and time resolution at the same time. To overcome this problem, windowed Fourier transform or short-time Fourier transform was introduced. Although the short-time Fourier transform has the ability to provide time information, a complete multiresolution analysis is not possible. Wavelet is a solution to the multiresolution problem. A wavelet has the important property of not having a fixed-width sampling window. The wavelet transform can be classified into (i) continuous wavelet transform and (ii) discrete wavelet transform. The v