With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

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BackgroundOne of the potentially important applications of three-dimensional (3D) intracranial vessel wall (IVW) cardiovascular magnetic resonance (CMR) is to monitor disease progression and regression via quantitative measurement of IVW morphology during medical management or drug development. However, a prerequisite for this application is to validate that IVW morphologic measurements based on the modality are reliable. In this study we performed comprehensive reliability analysis for the recently proposed whole-brain IVW CMR technique.MethodsThirty-four healthy subjects and 10 patients with known intracranial atherosclerotic disease underwent repeat whole-brain IVW CMR scans. In 19 of the 34 subjects, two-dimensional (2D) turbo spin-echo (TSE) scan was performed to serve as a reference for the assessment of vessel dimensions. Lumen and wall volume, normalized wall index, mean and maximum wall thickness were measured in both 3D and 2D IVW CMR images. Scan-rescan, intra-observer, and inter-observer reproducibility of 3D IVW CMR in the quantification of IVW or plaque dimensions were respectively assessed in volunteers and patients as well as for different healthy subjectsub-groups (i.e. < 50 and ≥ 50 years). The agreement in vessel wall and lumen measurements between the 3D technique and the 2D TSE method was also investigated. In addition, the sample size required for future longitudinal clinical studies was calculated.ResultsThe intra-class correlation coefficient (ICC) and Bland-Altman plots indicated excellent reproducibility and inter-method agreement for all morphologic measurements (All ICCs > 0.75). In addition, all ICCs of patients were equal to or higher than that of healthy subjects except maximum wall thickness. In volunteers, all ICCs of the age group of ≥50 years were equal to or higher than that of the age group of < 50 years. Normalized wall index and mean and maximum wall thickness were significantly larger in the age group of ≥50 years. To detect 5% - 20% difference between placebo and treatment groups, normalized wall index requires the smallest sample size while lumen volume requires the highest sample size.ConclusionsWhole-brain 3D IVW CMR is a reliable imaging method for the quantification of intracranial vessel dimensions and could potentially be useful for monitoring plaque progression and regression.Electronic supplementary materialThe online version of this article (10.1186/s12968-018-0453-z) contains supplementary material, which is available to authorized users.

Photothermal therapy (PTT) as a relatively new cancer treatment method has attracted worldwide attention. Previous research on PTT has focused on its therapy efficiency and selectivity. The early prognosis of PTT, which is pivotal for the assessment of the treatment and the therapy stratification, however, has been rarely studied. In the present study, we investigated diffusion-weighted magnetic resonance imaging (DW-MRI) as a tool for therapy monitoring and early prognosis of PTT. To this end, we injected PEGylated graphene oxide (GO-PEG) or iron oxide deposited graphene oxide (GO-IONP-PEG) to 4T1 tumor models and irradiated the tumors at different drug-light intervals to induce PTT. For GO-IONP-PEG injected animals, we also included therapy arms where an external magnetic field was applied to the tumors to improve the delivery of the nanoparticle transducers. DW-MRI was performed at different time points after PTT and the tumor apparent diffusion coefficients (ADCs) were analyzed and compared. Our studies show that photothermal agents, magnetic guidance, and drug-light intervals can all affect PTT treatment efficacy. Impressively, ADC value changes at early time points after PTT (less than 48 h) were found to be well-correlated with tumor growth suppression that was apparent days or weeks later. The changes were most sensitive to conditions that can extend the survival for more than 4 weeks, in which cases the 48 h ADC values were increased by more than 80%. These studies demonstrate for the first time that DW-MRI can be an accurate prognosis tool for PTT, suggesting an important role it can play in the future PTT evaluation and clinical translation of the modality.

Texture classification has been extensively studied in computer vision. Recent research shows that the combination of Fisher vector (FV) encoding and convolutional neural network (CNN) provides significant improvement in texture classification over the previous feature representation methods. However, by truncating the CNN model at the last convolutional layer, the CNN-based FV descriptors would not incorporate the full capability of neural networks in feature learning. In this study, we propose that we can further transform the CNN-based FV descriptors in a neural network model to obtain more discriminative feature representations. In particular, we design a locally-transferred Fisher vector (LFV) method, which involves a multi-layer neural network model containing locally connected layers to transform the input FV descriptors with filters of locally shared weights. The network is optimized based on the hinge loss of classification, and transferred FV descriptors are then used for image classification. Our results on three challenging texture image datasets show improved performance over the state-of-the-art approaches.