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Automated detection of vision threatening eye disease based on high resolution retinal fundus images requires accurate segmentation of the blood vessels. In this regard, detection and segmentation of finer vessels, which are obscured by a considerable degree of noise and poor illumination, is particularly challenging. These noises include (systematic) additive noise and multiplicative (speckle) noise, which arise due to various practical limitations of the fundus imaging systems. To address this inherent issue, we present an efficient unsupervised vessel segmentation strategy as a step towards accurate classification of eye diseases from the noisy fundus images. To that end, an ensemble block matching 3D (BM3D) speckle filter is proposed for removal of unwanted noise leading to improved detection. The BM3D-speckle filter, despite its ability to recover finer details (i.e., vessels in fundus images), yields a pattern of checkerboard artifacts in the aftermath of multiplicative (speckle) noise removal. These artifacts are generally ignored in the case of satellite images; however, in the case of fundus images, these artifacts have a degenerating effect on the segmentation or detection of fine vessels. To counter that, an ensemble of BM3D-speckle filter is proposed to suppress these artifacts while further sharpening the recovered vessels. This is subsequently used to devise an improved unsupervised segmentation strategy that can detect fine vessels even in the presence of dominant noise and yields an overall much improved accuracy. Testing was carried out on three publicly available databases namely Structured Analysis of the Retina (STARE), Digital Retinal Images for Vessel Extraction (DRIVE) and CHASE_DB1. We have achieved a sensitivity of 82.88, 81.41 and 82.03 on DRIVE, SATARE, and CHASE_DB1, respectively. The accuracy is also boosted to 95.41, 95.70 and 95.61 on DRIVE, SATARE, and CHASE_DB1, respectively. The performance of the proposed methods on images with pathologies was observed to be more convincing than the performance of similar state-of-the-art methods.

Although there has been a lot of progress in the general area of signal denoising, noise removal remains a very challenging problem in real-world communication systems. Denoising algorithms are typically used during the image preprocessing phase and are chosen based on the type of image, as a specific algorithm may work for a given noise but not for another one. Moreover, an algorithm can sometimes consider crucial information as being noise and eliminate it, hence the importance of careful selection and tuning of denoising algorithms. Denoising algorithms built on discrete wavelet transform decomposes signals into different frequency resolution levels. Thresholding is then applied to higher frequency components which generally correspond to noise to eliminate this one. In this paper, we review wavelet-based denoising methods and compare their performance based on metrics such as peak signal-to-noise ratio (PSNR) and Structural Similarity (SSIM). This work aims to find the best wavelet denoising algorithm using Peak these metrics. The common Matlab images such as cameraman, barbara, coins, and eight are used for our test. From these tests, the BM3DM_DWT method was found to be the simplest and most efficient for denoising.

In this paper, a novel denoising approach based on optimal trilateral filtering using Grey Wolf Optimization (GWO) is proposed. At first, a database of noisy images are generated by adding Gaussian noise, Salt & Pepper noise and Random noise to the captured image. The filtering of noisy images are performed by Block-matching and 3D filtering (BM3D) algorithm over the components of image obtained through the moving frame approach. Then, using optimal trilateral filtering, the denoised images are reconstructed. Therefore, by using a two-level filtering approach such as Moving frame-based Block-matching and 3D filtering (BM3D) and Optimal trilateral filtering the noisy images are decomposed. The proposed optimal trilateral filter employs Grey Wolf Optimization algorithm for selecting the parameters optimally to improve the efficiency of filtering method which also reduces the time required for manual computation. The performance of the proposed image denoising algorithm is analyzed using multiple datasets and the analysis of results were done in contrast with existing conventional approaches. The results validated that the optimal trilateral filtering approach outperforms other conventional methods in terms of Mean-Square Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR).

Multi-view or light field images have recently gained much attraction from academic and commercial fields to create breakthroughs that go beyond simple video-watching experiences. Immersive virtual reality is an important example. High image quality is essential in systems with a near-eye display device. The compression efficiency is also critical because a large amount of multi-view data needs to be stored and transferred. However, noise can be easily generated during image capturing, and these noisy images severely deteriorate both the quality of experience and the compression efficiency. Therefore, denoising is a prerequisite to produce multi-view-based image contents. In this paper, the structural characteristics of linear multi-view images are fully utilized to increase the denoising speed and performance as well as to improve the compression efficiency. Assuming the sequential processes of denoising and compression, multi-view geometry-based denoising is performed keeping the temporal correlation among views. Experimental results show the proposed scheme significantly improves the compression efficiency of denoised views up to 76.05%, maintaining good denoising quality compared to the popular conventional denoise algorithms.

Noise, which is commonly generated in low-light environments or by low-performance cameras, is a major cause of the degradation of compression efficiency. In previous studies that attempted to combine a denoise algorithm and a video encoder, denoising was used independently of the code for pre-processing or post-processing. However, this process must be tightly coupled with encoding because noise affects the compression efficiency greatly. In addition, this represents a major opportunity to reduce the computational complexity, because the encoding process and a denoise algorithm have many similarities. In this paper, a simple, add-on denoising scheme is proposed through a combination of high-efficiency video coding (HEVC) and block matching three-dimensional collaborative filtering (BM3D) algorithms. It is known that BM3D has excellent denoise performance but that it is limited in its use due to its high computational complexity. This paper employs motion estimation in HEVC to replace the block matching of BM3D so that most of the time-consuming functions are shared. To overcome the challenging algorithmic differences, the hierarchical structure in HEVC is uniquely utilized. As a result, the computational complexity is drastically reduced while the competitive performance capabilities in terms of coding efficiency and denoising quality are maintained.

AS a medical imaging technology, dual-energy computed tomography (DECT) has attracted attention owing to its higher accuracy in terms of material separation compared with conventional single-energy CT imaging. A multi-material decomposition (MMD) technique that can separate two or more materials from DECT images has recently been developed. Although a highly accurate material separation can be obtained when MMD technology is applied to CT images, the inevitable addition of noise to the image is a disadvantage. Thus, block-matching and 3D filtering (BM3D) denoising algorithm was modeled to evaluate its applicability to CT images of materials separated using MMD technology. The simulation results confirmed that when the BM3D denoising algorithm was applied to CT images separated from the material using MMD technology, the root mean square (RMS), structural similarity index, and coefficient of variation (COV) were improved by 92.44%, 16.44%, and 92.82%, respectively, compared to when only MMD was applied. In addition, the experimental results showed the same tendencies as the simulations, and volume fraction accuracy (VFA) along with the RMS and COV evaluation parameters showed the best results when BM3D was applied to CT images. Improved results were obtained using the BM3D denoising algorithm when applying the MMD technique to DECT images.

This Letter proposes a combination of target detection and block-matching 3D filter for despeckling SAR images. The proposed method is able to effectively preserve targets, such as the edges and dots of synthetic aperture radar (SAR) images, whilst removing noises. In the first step of our proposed method, based on despekling results of bilateral filtering and edge detection of a canny operator, some targets are detected and removed from an SAR image. The second step uses BM3D for denoising the targets-removed image. Finally, the removed targets are added to the despeckled targets-removed image and desirable results can be achieved.