Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.

Image denoising is considered a salient pre-processing step in sophisticated imaging applications. Over the decades, numerous studies have been conducted in denoising. Recently proposed Block matching and 3D (BM3D) filtering added a new dimension to the study of denoising. BM3D is the current state-of-the-art of denoising and is capable of achieving better denoising as compared to any other existing method. However, there is room to improve BM3D to achieve high-quality denoising. In this study, to improve BM3D, we first attempted to improve the Wiener filter (the core of BM3D) by maximizing the structural similarity (SSIM) between the true and the estimated image, instead of minimizing the mean square error (MSE) between them. Moreover, for the DC-only BM3D profile, we introduced a 3D zigzag thresholding. Experimental results demonstrate that regardless of the type of the image, our proposed method achieves better denoising performance than that of BM3D.

Accurate delineation of object borders is highly desirable in echocardiography, especially at the left ventricle. Among other model-based techniques, active contours (or snakes) provide a unique and powerful approach to image analysis. In this work, we propose the use of a new external energy for a gradient vector flow (GVF) snake, being the optical flow of a moving sequence (modeling the mechanical movement of the heart). This external energy can provide additional information to the active contour model garnering adequate results for moving sequences. An automatic iterative primitive shape prior was also applied in order to further improve the results of a GVF snake, when dealing with especially noisy echocardiographic images. Results were compared with expert-defined segmentations yielding acceptable sensitivity, precision rate and overlap ratio performance.

Lossy image compression techniques aim at encoding images with a minimal representation. During this process, some visually useful information may be lost. Assessing the information loss in decompressed images is not an easy task. In this paper, a new quantitative image-quality measure is introduced. This new measure incorporates information theory into the most commonly used objective criterion (the mean square error). The new measure has been tested by experiments performed on a wide variety of images. The results show an increase in the correlation between subjective rating by human observers and the normalized mean square error after applying the new measure.

The swallowing process involves triggering the movements of a number of muscles in the throat that transports the food from the mouth to the stomach successfully and at the same time prevents it from getting into the airway and the lung. In order to detect abnormalities in the swallowing process, radiologists use a technique called videofluoroscopic swallowing study. It is a video of X-ray images that are taken while the patient swallows food, which is later visually inspected by the radiologist to evaluate the patient's swallowing ability. It has been reported that measuring the movement of the hyoid bone plays an important role in the evaluation process. However, due to the subjective nature of visual inspection, radiologists have difficulty reaching unanimous decision about the outcome of the evaluation. In this research, a semi-automatic method is proposed which tracks the hyoid bone and quantifies its movement. Using a classification-based approach, the proposed method automatically identifies the region of interest before identifying the hyoid bone. This allows limiting image processing procedures to the relevant area in the image. Results show that the proposed method identifies and tracks the hyoid bone with significant accuracy.