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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

In the era of rapid growth of technologies, image matting plays a key role in image and video editing along with image composition. In many significant real-world applications such as film production, it has been widely used for visual effects, virtual zoom, image translation, image editing and video editing. With recent advancements in digital cameras, both professionals and consumers have become increasingly involved in matting techniques to facilitate image editing activities. Image matting plays an important role to estimate alpha matte in the unknown region to distinguish foreground from the background region of an image using an input image and the corresponding trimap of an image which represents a foreground and unknown region. Numerous image matting techniques have been proposed recently to extract high-quality matte from image and video sequences. This paper illustrates a systematic overview of the current image and video matting techniques mostly emphasis on the current and advanced algorithms proposed recently. In general, image matting techniques have been categorized according to their underlying approaches, namely, sampling-based, propagation-based, combination of sampling and propagation-based and deep learning-based algorithms. The traditional image matting algorithms depend primarily on color information to predict alpha matte such as sampling-based, propagation-based or combination of sampling and propagation-based algorithms. However, these techniques mostly use low-level features and suffer from high-level background which tends to produce unwanted artifacts when color is same or semi-transparent in the foreground object. Image matting techniques based on deep learning have recently introduced to address the shortcomings of traditional algorithms. Rather than simply depending on the color information, it uses deep learning mechanism to estimate the alpha matte using an input image and the trimap of an image. A comprehensive survey on recent image matting algorithms and in-depth comparative analysis of these algorithms has been thoroughly discussed in this paper.

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Medical imaging is playing a pivotal role in the domain of medical diagnosis. But the major problems related to medical images is that most of the images (of different modalities) are suffered from noise and other quality‐related problems such as poor contrast, blurring, and difficulties in extracting appropriate information. Therefore, it is necessary to construct some techniques that could improve medical images in such a way that it will be ideal for diagnostic applications. Medical image enhancement is the process of increasing the contrast quality of intensity variations and improves the visual representation of medical images. To provide suitable interpretation and clearer image for the observers with reduced noise levels, this article proposes a novel competent medical image enhancement method based on morphologically processing of residuals using a special kernel. First, it combines linear low pass filtering with nonlinear technique that allows for selection of essential regions where edges will get well preserved. The selection of those regions is based on morphological processing of linear filter residuals and then aims to find significant regions specified by edges of high amplitude and appropriate size. The reconstructed regions are combined with the output of low pass filtering to recovers the original shape of edges. In addition, the method allows to control the contrast to avoid blurring while preserving significant image information. In the end, a special kernel is convolved with an image to obtain the sharper image. Experiments on the different types of medical images are performed and the results are compared using different standard evaluation metrics. The quantitative results obtained through proposed method for all the metrics are optimal compared to other competing methods. It is observed that the results on various test images affirm that the proposed method generates excellent image visual quality and significantly enhances the contrast of all images which helps with better diagnosis and treatment.