Image Quality Evaluation Metrics Research Articles

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels’ long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels’ correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Underwater Fish Object Detection with Degraded Prior Knowledge

Understanding fish distribution, behavior, and abundance is crucial for marine ecological research, fishery management, and environmental monitoring. However, the distinctive features of the underwater environment, including low visibility, light attenuation, water turbidity, and strong currents, significantly impact the quality of data gathered by underwater imaging systems, posing considerable challenges in accurately detecting fish objects. To address this challenge, our study proposes an innovative fish detection network based on prior knowledge of image degradation. In our research process, we first delved into the intrinsic relationship between visual image quality restoration and detection outcomes, elucidating the obstacles the underwater environment poses to object detection. Subsequently, we constructed a dataset optimized for object detection using image quality evaluation metrics. Building upon this foundation, we designed a fish object detection network that integrates a prompt-based degradation feature learning module and a two-stage training scheme, effectively incorporating prior knowledge of image degradation. To validate the efficacy of our approach, we develop a multi-scene Underwater Fish image Dataset (UFD2022). The experimental results demonstrate significant improvements of 2.4% and 2.5%, respectively, in the mAP index compared to the baseline methods ResNet50 and ResNetXT101. This outcome robustly confirms the effectiveness and superiority of our process in addressing the challenge of fish object detection in underwater environments.

BPG-based compression analysis of Poisson-noisy medical images

The subject matter is lossy compression using the BPG encoder for medical images with varying levels of visual complexity, which are corrupted by Poisson noise. The goal of this study is to determine the optimal parameters for image compression and select the most suitable metric for identifying the optimal operational point. The tasks addressed include: selecting test images sized 512x512 in grayscale with varying degrees of visual complexity, encompassing visually intricate images rich in edges and textures, moderately complex images with edges and textures adjacent to homogeneous regions, and visually simple images primarily composed of homogeneous regions; establishing image quality evaluation metrics and assessing their performance across different encoder compression parameters; choosing one or multiple metrics that distinctly identify the position of the optimal operational point; and providing recommendations based on the attained results regarding the compression of medical images corrupted by Poisson noise using a BPG encoder, with the aim of maximizing the restored image’s quality resemblance to the original. The employed methods encompass image quality assessment techniques employing MSE, PSNR, MSSIM, and PSNR-HVS-M metrics, as well as software modeling in Python without using the built-in Poisson noise generator. The ensuing results indicate that optimal operational points (OOP) can be discerned for all these metrics when the compressed image quality surpasses that of the corresponding original image, accompanied by a sufficiently high compression ratio. Moreover, striking a suitable balance between the compression ratio and image quality leads to partial noise reduction without introducing notable distortions in the compressed image. This study underscores the significance of employing appropriate metrics for evaluating the quality of compressed medical images and provides insights into determining the compression parameter Q to attain the BPG encoder’s optimal operational point for specific images. Conclusions. The scientific novelty of the findings encompasses the following: 1) the capability of all metrics to determine the OOP for images of moderate visual complexity or those dominated by homogeneous areas; MSE and PSNR metrics demonstrating superior results for images rich in textures and edges; 2) the research highlights the dependency of Q in the OOP on the average image intensity, which can be reasonably established for a given image earmarked for compression based on our outcomes. The compression ratios for images compressed at the OOP are sufficiently high, further substantiating the rationale for compressing images in close proximity to the OOP.

Image generation technology for functional occlusal pits and fissures based on a conditional generative adversarial network.

The occlusal surfaces of natural teeth have complex features of functional pits and fissures. These morphological features directly affect the occlusal state of the upper and lower teeth. An image generation technology for functional occlusal pits and fissures is proposed to address the lack of local detailed crown surface features in existing dental restoration methods. First, tooth depth image datasets were constructed using an orthogonal projection method. Second, the optimization and improvement of the model parameters were guided by introducing the jaw position spatial constraint, the L1 loss and the perceptual loss functions. Finally, two image quality evaluation metrics were applied to evaluate the quality of the generated images, and deform the dental crown by using the generated occlusal pits and fissures as constraints to compare with expert data. The results showed that the images generated using the network constructed in this study had high quality, and the detailed pit and fissure features on the crown were effectively restored, with a standard deviation of 0.1802mm compared to the expert-designed tooth crown models.

Low-light image enhancement based on virtual exposure

Under poor illumination, the image information captured by a camera is partially lost, which seriously affects the visual perception of the human. Inspired by the idea that the fusion of multiexposure images can yield one high-quality image, an adaptive enhancement framework for a single low-light image is proposed based on the strategy of virtual exposure. In this framework, the exposure control parameters are adaptively generated through a statistical analysis of the low-light image, and a virtual exposure enhancer constructed by a quadratic function is applied to generate several image frames from a single input image. Then, on the basis of generating weight maps by three factors, i.e., contrast, saturation and saliency, the image sequences and weight images are transformed by a Laplacian pyramid and Gaussian pyramid, respectively, and multiscale fusion is implemented layer by layer. Finally, the enhanced result is obtained by pyramid reconstruction rule. Compared with the experimental results of several state-of-the-art methods on five datasets, the proposed method shows its superiority on several image quality evaluation metrics. This method requires neither image calibration nor camera response function estimation and has a more flexible application range. It can weaken the possibility of overenhancement, effectively avoid the appearance of a halo in the enhancement results, and adaptively improve the visual information fidelity.

Renal Pathology Images Segmentation Based on Improved Cuckoo Search with Diffusion Mechanism and Adaptive Beta-Hill Climbing.

Lupus Nephritis (LN) is a significant risk factor for morbidity and mortality in systemic lupus erythematosus, and nephropathology is still the gold standard for diagnosing LN. To assist pathologists in evaluating histopathological images of LN, a 2D Rényi entropy multi-threshold image segmentation method is proposed in this research to apply to LN images. This method is based on an improved Cuckoo Search (CS) algorithm that introduces a Diffusion Mechanism (DM) and an Adaptive β-Hill Climbing (AβHC) strategy called the DMCS algorithm. The DMCS algorithm is tested on 30 benchmark functions of the IEEE CEC2017 dataset. In addition, the DMCS-based multi-threshold image segmentation method is also used to segment renal pathological images. Experimental results show that adding these two strategies improves the DMCS algorithm's ability to find the optimal solution. According to the three image quality evaluation metrics: PSNR, FSIM, and SSIM, the proposed image segmentation method performs well in image segmentation experiments. Our research shows that the DMCS algorithm is a helpful image segmentation method for renal pathological images.

Transfer learning framework for low-dose CT reconstruction based on marginal distribution adaptation in multi-scale.

With the increasing use of CT in clinical practice, limiting CT radiation exposure to reduce potential cancer risks has become one of the important directions of medical imaging research. As the dose decreases, the reconstructed CT image will be severely degraded by projection noise. As an important method of image processing, supervised deep learning has been widely used in the restoration of low-dose CT(LDCT) in recent years. However, the normal-dose CT (NDCT) corresponding to a specific LDCT (it is regareded as the label of the LDCT, which is necessary for supervised learning) are very difficult to obtain. So that the application of supervised learning methods in LDCT reconstruction is limited. It is necessary to construct a unsupervised deep learning framework for LDCT reconstruction that does not depend on paired LDCT-NDCTdatasets. We presented an unsupervised learning framework for the transferring from the identity mapping to the low-dose recontruction task, called Marginal Distribution Adaptation in Multi-scale (MDAM). For NDCTs as source domain data, MDAM is an identity map with two parts: firstly, it establishes a dimensionality reduction mapping, which can obtain the same feature distribution from NDCTs and LDCTs; and then NDCTs is retrieved by reconstructing the image overview and details from the low-dimensional features. For the purpose of the feature transfer between source domain and target domain (LDCTs), we introduce the multi-scale feature extraction in the MDAM, and then eliminate differences in probability distributions of these multi-scale features between NDCTs and LDCTs through wavelet decomposition and domain adaptationlearning. Image quality evaluation metrics and subjective quality scores show that, as an unsupervised method, the performance of the MDAM approaches or even surpasses some state-of-the-art supervised methods. Especially, MDAM has been favorably evaluated in terms of noise suppression, structural preservation, and lesiondetection. We demonstrated that, the MDAM framework can reconstruct corresponding NDCTs from LDCTs with high accuracy, and without relying on any labeles. Moreover, it is more suitable for clinical application compared with supervised learningmethods. This article is protected by copyright. All rights reserved.

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using our new publicly-available Challenging Dataset for Underwater Image Enhancement (CDUIE). The collected dataset is comprised of 85 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/ashrafrepo/underwater-image-enhancement</uri> .

Turbid Underwater Image Enhancement Based on Parameter-Tuned Stochastic Resonance

In turbid water, the attenuation and scattering of light caused by scatterers make underwater optical images degraded, blurred, and contrast reduced, limiting the extraction and analysis of information from images. To address such problems, a turbid underwater image enhancement method based on parameter-tuned stochastic resonance (SR) is proposed in this article. First, an SR algorithm framework for underwater image enhancement is constructed, including the dimensionality reduction and normalization of input images, the solution and parameter optimization of the SR system, the dimensionality upgrading of output images, etc. This framework can apply the SR's ability to enhance weak signals to the enhancement of turbid underwater images. Second, to measure the performance of the system, a synthetic turbid underwater image data set (UWCHIC) is constructed using the underwater imaging model and an image set with simulated scatterers. Based on this data set, the relationship between various image quality evaluation metrics and system parameters is analyzed, and then the suitable no-reference (NR) metrics for system performance evaluation are selected and an adaptive parameter tuning strategy of the SR system is proposed to guide the image enhancement. Lastly, the proposed method is evaluated on the UWCHIC, a dataset to evaluate underwater image restoration methods (TURBID), marine underwater environment database (MUED), and underwater image enhancement benchmark (UIEB) data sets and the turbid underwater images captured from natural waters. Different experimental evaluations demonstrated that the proposed method not only effectively enhances the visual quality of turbid underwater images but also improves the performance of downstream vision tasks.

Investigation on improving performance of adaptive beamformer by statistical analysis of ultrasonic echoes

Minimum variance (MV) beamformers have been introduced in medical ultrasound imaging to improve image quality. In most cases, the MV beamformers have been investigated in terms of resolution improvement. However, the contrast-to-noise ratio (CNR) is also a clinically important metrics and gathers attention recently. In this study, we examined the diagonal loading parameter σ in MV beamforming and determined its appropriate value by evaluating image quality evaluation metrics including CNR. In order to further improve the image quality, a method for determining the value of σ based on the difference in statistical properties of received ultrasonic echo signals was also investigated. The phantom experimental results showed that the proposed method achieved a better CNR than the conventional MV beamformer while keeping resolution significantly better than that in delay-and-sum beamforming.

A genetic programming-based convolutional neural network for image quality evaluations

Monitoring the perceptual quality of digital images is fundamentally important since digital image transmissions through the Internet continue to increase exponentially. Many automatic image quality evaluation (IQE) metrics have been developed based on image features correlated to image distortions; however, those metrics are only effective on particular image distortion types. In recent years, convolutional neural network (CNNs) have been developed for IQEs. These CNNs first capture image features from distorted images; image qualities are predicted based on the captured image features. Since the CNN weights are randomly initialized and are updated with respect to a loss function, image features which are strongly correlated to image quality are not guaranteed to be captured. In this paper, a hybrid deep neural network (DNN) is proposed by integrating image quality metrics to capture image features which are correlated to image quality; the approach guarantees that significant image features are included to predict image quality. Also, a tree-based classifier namely geometric semantic genetic programming is proposed to perform the overall predictions by incorporating CNN predictions and image features; the approach is simpler than the fully connected network but is able to model the nonlinear image qualities. The performance of the proposed hybrid DNN is evaluated by an image quality database with 3000 distorted images. The mean correlation achieved by the proposed hybrid DNN is 0.57 which is higher than the other tested methods. Experimental results with the t- test, F-test and Tueky’s range tests show that the proposed hybrid DNN achieves more accurate image predictions with a 99.9% confidence level, compared to the state-of-the-art IQE metics and the most recently developed CNN for IQEs.

Chemotherapy response prediction with diffuser elapser network

In solid tumors, elevated fluid pressure and inadequate blood perfusion resulting from unbalanced angiogenesis are the prominent reasons for the ineffective drug delivery inside tumors. To normalize the heterogeneous and tortuous tumor vessel structure, antiangiogenic treatment is an effective approach. Additionally, the combined therapy of antiangiogenic agents and chemotherapy drugs has shown promising effects on enhanced drug delivery. However, the need to find the appropriate scheduling and dosages of the combination therapy is one of the main problems in anticancer therapy. Our study aims to generate a realistic response to the treatment schedule, making it possible for future works to use these patient-specific responses to decide on the optimal starting time and dosages of cytotoxic drug treatment. Our dataset is based on our previous in-silico model with a framework for the tumor microenvironment, consisting of a tumor layer, vasculature network, interstitial fluid pressure, and drug diffusion maps. In this regard, the chemotherapy response prediction problem is discussed in the study, putting forth a proof of concept for deep learning models to capture the tumor growth and drug response behaviors simultaneously. The proposed model utilizes multiple convolutional neural network submodels to predict future tumor microenvironment maps considering the effects of ongoing treatment. Since the model has the task of predicting future tumor microenvironment maps, we use two image quality evaluation metrics, which are structural similarity and peak signal-to-noise ratio, to evaluate model performance. We track tumor cell density values of ground truth and predicted tumor microenvironments. The model predicts tumor microenvironment maps seven days ahead with the average structural similarity score of 0.973 and the average peak signal ratio of 35.41 in the test set. It also predicts tumor cell density at the end day of 7 with the mean absolute percentage error of 2.292pm 1.820.

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Image dehazing is still an open research topic that has been undergoing a lot of development, especially with the renewed interest in machine learning-based methods. A major challenge of the existing dehazing methods is the estimation of transmittance, which is the key element of haze-affected imaging models. Conventional methods are based on a set of assumptions that reduce the solution search space. However, the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image. In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method. The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions. This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks (CNNs) with a Parallax Attention Mechanism (PAM). It uses the differential pixel-based variance in order to estimate transmittance. The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map. The transmission map is also improved based on improved Markov random field (MRF) energy functions. We used different images to test the proposed algorithm. The entropy value of the proposed method was 7.43 and 7.39, a percent increase of and , respectively, compared to the best existing results. The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics. The proposed approach's drawback, an over-reliance on real ground truth images, is also investigated. The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

The Development of Three Image Quality Evaluation Metrics Based on a Comprehensive Dataset

In this study, a large scale experiment was carried out to assess the image quality of 2266 images using categorical judgement method by 20 observers. These images were rendered in color contrast, chroma, colorfulness, lightness, and vividness directions. The results were used to derive three No-Reference (NR) Image Quality Estimation Models (IQEMs). The first model was based on color science, (different scales in CIELAB). The second model was a Neural Network model while the third model was a statistics model based on color appearance attributes. Their performances were evaluated using two databases, those developed at Zhejiang University and those available from the public databases in terms of correlation coefficients between the objective and predicted image quality scores.

Artifact correction in low‐dose dental CT imaging using Wasserstein generative adversarial networks

In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

Wavelet Decomposition–Based Speckle Reduction Method for Ultrasound Images by Using Speckle-Reducing Anisotropic Diffusion and Hybrid Median

Ultrasound imaging is considered as a common facility of imaging techniques used in medical field because of its benefits, but the quality of ultrasound image is affected and degraded by the type of noise generated by the backscattered signals that interfere with each other and introduce a random interference pattern. This pattern is called speckle. In this article, we develop a new denoising method by introducing a speckle-reducing anisotropic diffusion filter into 4 sub-band images gained by using 2-dimensional wavelet decomposition. Moreover, according to frequency content on each sup band, we use hybrid median filter to get a full advantage of using nonlinear filtering in speckle noise. Finely to improve the quality of image and the overall performance, total variation is used in view of it is capability of brightness equality and edges preserving. The experimental result shows that the proposed method is highly effective in noise separation as well as edges preserving based on compression between linear scaling of the gray-level values despeckle filter, speckle-reducing anisotropic diffusion, median, hybrid median filter, and wavelet filters using image quality evaluation metrics.

Image enhancement for outdoor long‐range surveillance using IQ‐learning multiscale Retinex

The visible light camera-based long-range surveillance always suffers from the complex atmosphere. When applying some traditional image enhancement methods, the computational effects behave limited because of their poor environment adaptability. To conquer that problem, a blind image quality (IQ) learning-based multiscale Retinex, i.e. the IQ-learning multiscale Retinex, is proposed. First, a series of typical degenerated images are collected. Second, several blind IQ evaluation metrics are computed for the dataset above. They are the image brightness degree, the image region contrast degree, the image edge blur degree, the image colour quality degree, and the image noise degree. Third, a wavelet transform multi-scale Retinex (WT_MSR) is used to carry out the basic image enhancement. A kind of optimal enhancement is implemented by the subjective evaluation and tuning of multiple optimal control parameters (MOCPs) of WT_MSR for these degenerated dataset. Fourth, the back propagation neural network (BPNN) is used to build a connection between the IQ metrics and the MOCPs. Finally, when a new image is captured, this system will compute its IQ metrics and estimate the MOCPs for the WT_MSR by BPNN; then a kind of optimal enhancement can be realised. Many outdoor applications have shown the effectiveness of proposed method.

From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications

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.

Damage Degree Evaluation of Earthquake Area Using UAV Aerial Image

An Unmanned Aerial Vehicle (UAV) system and its aerial image analysis method are developed to evaluate the damage degree of earthquake area. Both the single-rotor and the six-rotor UAVs are used to capture the visible light image of ground targets. Five types of typical ground targets are considered for the damage degree evaluation: the building, the road, the mountain, the riverway, and the vegetation. When implementing the image analysis, first the Image Quality Evaluation Metrics (IQEMs), that is, the image contrast, the image blur, and the image noise, are used to assess the imaging definition. Second, once the image quality is qualified, the Gray Level Cooccurrence Matrix (GLCM) texture feature, the Tamura texture feature, and the Gabor wavelet texture feature are computed. Third, the Support Vector Machine (SVM) classifier is employed to evaluate the damage degree. Finally, a new damage degree evaluation (DDE) index is defined to assess the damage intensity of earthquake. Many experiment results have verified the correctness of proposed system and method.

Despeckle Filtering for Ultrasound Imaging and Video, Volume II: Selected Applications, Second Edition

In ultrasound imaging and video visual perception is hindered by speckle multiplicative noise that degrades the quality. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image/video segmentation, texture analysis and encoding in ultrasound imaging and video. The goal of the first book (book 1 of 2 books) was to introduce the problem of speckle in ultrasound image and video as well as the theoretical background, algorithmic steps, and the MatlabTM for the following group of despeckle filters: linear despeckle filtering, non-linear despeckle filtering, diffusion despeckle filtering, and wavelet despeckle filtering. The goal of this book (book 2 of 2 books) is to demonstrate the use of a comparative evaluation framework based on these despeckle filters (introduced on book 1) on cardiovascular ultrasound image and video processing and analysis. More specifically, the despeckle filtering evaluation framework is based on texture analysis, image quality evaluation metrics, and visual evaluation by experts. This framework is applied in cardiovascular ultrasound image/video processing on the tasks of segmentation and structural measurements, texture analysis for differentiating between two classes (i.e. normal vs disease) and for efficient encoding for mobile applications. It is shown that despeckle noise reduction improved segmentation and measurement (of tissue structure investigated), increased the texture feature distance between normal and abnormal tissue, improved image/video quality evaluation and perception and produced significantly lower bitrates in video encoding. Furthermore, in order to facilitate further applications we have developed in MATLABTM two different toolboxes that integrate image (IDF) and video (VDF) despeckle filtering, texture analysis, and image and video quality evaluation metrics. The code for these toolsets is open source and these are available to download complementary to the two monographs.