Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input source data. In this paper, we have demonstrated a comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail. The reference-based and non-reference-based image quality evaluation metrics are summarized together with recent trends in image fusion. Several image fusion applications in various fields have also been reported. It has been stated that though a lot of singular fusion techniques seemed to have given optimum results, the focus of researchers is shifting toward amalgamated or hybrid fusion techniques, which could harness the attributes of both multi-scale and non-multi-scale decomposition methods. Toward the end, the review is concluded with various open challenges for researchers. Thus, the descriptive study in this paper would form basis for stimulating and nurturing advanced research ideas in image fusion.

The current literature documents a plethora of image denoising techniques in the fields of medical imaging, remote sensing, biometrics, surveillance and vegetation mapping. Therefore it is important to have brief insight into various types of noises in different type of images, for instance medical images, remote sensing images and natural images. This article encompasses the basic definition, history, usage and type of noise affecting some of the major types of imaging modalities. Besides this a brief discussion on the type of noise prevailing in remote sensing and natural images is also given. While designing an effective image denoising algorithm, one needs to be acquainted with the prior information about the noise prevalent in various types of images. Further, a brief idea about the basic principle, outlook, contrast levels and application of medical imaging modalities has also been presented in the context of this article.­­­­­­

Medical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods

Digital images are an extremely powerful and widely used medium of communication. They are able to represent very intricate details about the world that surrounds us in a very easy, compact and readily available manner. Due to the innate advances in the acquisition devices such as bio-sensors and remote sensors, huge amount of data is accessible for the further processing and information extraction. The need to efficiently process this immense amount of information has given rise to the emergence of the popular disciplines like image processing, image analysis, and computer vision and image fusion. This article gives a brief insight into basic understanding and significance of image fusion.