The main purpose of this paper was to introduce an efficient algorithm for fault identification in fruits images. First, input image was de-noised using the combination of Block Matching and 3D filtering (BM3D) and Principle Component Analysis (PCA) model. Afterward, in order to reduce the size of images and increase the execution speed, refined Discrete Cosine Transform (DCT) algorithm was uti...

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following ”SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restore...

This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. So denoising boosts the true signal strength. Under Bayesian framework, we have used two different priors: sparsity and clusterdness in...

Many important imaging problems in material science involve reconstruction of images containing repetitive non-local structures. Model-based iterative reconstruction (MBIR) could in principle exploit such redundancies through the selection of a log prior probability term. However, in practice, determining such a log prior term that accounts for the similarity between distant structures in the i...

With the widespread application of convolutional neural networks (CNNs), traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. First, they computationally demanding, which makes their deployment especially difficult for mobile terminals. Second, experimental evidence shows that often over-smooth regular textures present in images, contrast to non-lo...

The D-AMP methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery. But AMP diverges with small deviations from the i.i.d.-Gaussian assumption on the measurement matrix. Recently, the VAMP algorithm has been proposed to fix this problem. In this work, we ...

As quotidian use of sophisticated cameras surges, people in modern society are more interested capturing fine-quality images. However, the quality images might be inferior to people's expectations due noise contamination Thus, filtering out while preserving vital image features is an essential requirement. Existing denoising methods have assumptions, on probability distribution which contaminat...

We propose and experimentally demonstrate a scheme for accelerated fast BOTDA. The effect of signal-to-noise ratio (SNR) on recovery performance compressed sensing is simulated analyzed, it found that reduction in SNR requires much larger frequency data to recover the original Brillouin gain spectrum (BGS). To enable high probability, Block-Matching 3D filtering (BM3D) algorithm employed enhanc...

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional methods hardly achieve the effect of deep learning methods. Based processing techniques, this paper proposes a new hybrid model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain first denoised image. weighted kernel norm minimization (WNNM) non-subsa...

Patch-based approaches such as 3D block matching (BM3D) and non-local Bayes (NLB) are widely accepted filters for removing Gaussian noise from single-frame images. In this work, we propose three extensions these when there exist multiple frames of the same scene. The first them employs reference patches on every frame instead a commonly used single method, thus utilizing complete available info...