A comprehensive survey on impulse and Gaussian denoising filters for digital images

Comparative analysis of median filter and its variants for removal of impulse noise from gray scale images

The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual weighted lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes the previous work and provides a better approximation to the rank minimization problem for original matrix as well as the l0-norm minimization problem for sparse data. Moreover, an approximate closed-form solution is introduced to solve the lp-norm minimization, which has more stability in the nonconvex optimization and provides a more accurate estimation for the low-rank and sparse matrix recovery problem. We then apply the DWLP model to remove salt-and-pepper noise by exploiting the image nonlocal self-similarity. Both qualitative and quantitative experiments demonstrate that the proposed method outperforms other state-of-the-art methods. In terms of PSNR evaluation, our DWLP achieves about 7.188dB, 5.078dB, 3.854dB, 2.536dB and 0.158dB improvements over the current WSNM-RPCA under 10\% to 50\% salt-and-pepper noise with an interval 10\% respectively.

Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal.

Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.

https://www.beei.org/index.php/EEI/article/download/1458/1289

Comparison of human detection using background subtraction and frame difference

Digital imaging equipment is used for a variety of purposes in a wide range of fields in society and is becoming an important element of the 4th industrial revolution. Imaging equipment data is exposed to noise from various causes, and this noise affects accuracy of the equipment, causing errors and lowering reliability. In the present paper, an algorithm based on directional effective pixels is proposed to effectively remove high-density Salt and Pepper noise. Conventional methods show that performance decreases as Salt and Pepper noise density increases. To the contrary, the proposed method has noise-removal performance superior to those of conventional methods, by performing de-noising which considers directional effective pixels even in a high-density Salt and Pepper noise environment. Experimental results show that the proposed algorithm is superior to the existing methods, and performance is verified through enlarged images. 키워드 Salt and Pepper 잡음, 방향성, 잡음 제거, PSNR

Salt and Pepper Noise Removal Algorithm using Directional Effective Pixels

Noise removal in an image is important in image processing because noise can bring a great deal amount of problems such as degraded the quality image and loss of crucial information in image. Therefore, image filtering technique has been introduced to overcome the problems. One of the common noises in image is Salt and Pepper noise. A lot of studies have been conducted by researchers to remove this noise efficiently. However, most of the studies have difficulties in removing this noise especially at high density. Therefore, this research is conducted to improve image filtering technique and preserve the originality of the pixel. The proposed technique is developed based on Decision Based Algorithm and the performance is compared with Median Filter, Hybrid Median Filter, Adaptive Median Filter and Ben Filter. Three images are used which are the lead frame, fruit and baboon images and the metric of comparison is determined by using Mean Square Error method. The result of this study has shown that the proposed filter can remove Salt and Pepper noise efficiently at different density and restored the originality of the pixel.

Salt and pepper noise removal by using improved decision based algorithm

Image filtering technique plays a very important role in digital image processing. It is one of the major steps in image enhancement and restoration. This filtering technique can remove noise and preserve the details of the image for feature extraction processes. However, filtering process can still be considered as a huge challenge for image filtering technique. Common noises in the image such as Salt & Pepper, Gaussian, Speckle, and Poisson Noise are still posing problems in filtering process where the quality and the originality of the images can be degraded and disturbed. Meanwhile, a single filtering technique is usually fit to deal with only certain specific noise. This paper presents an enhanced Hybrid Median Filter (H6F) technique to improve image filtering process. The technique involves 3x3 sub-mask and determination of new pixel value from the median value of the three steps which are the median calculation of ‘+’-neighbours, median calculation of all sub-masks and selection of centre pixel value. The H6F technique has been computed on lead frame inspection system. The results have shown that the technique has been able to remove multi-type of noises efficiently and produce exceptionally low Mean-Square Error (MSE) while consuming the acceptable amount of execution time when compared to other filtering techniques.

Multi-type Noise Removal in Lead Frame Image Using Enhanced Hybrid Median Filter

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Muhammad Syafiq Alza Alias

Papers

Salt and pepper noise removal by using improved decision based algorithm

Multi-type Noise Removal in Lead Frame Image Using Enhanced Hybrid Median Filter

Improved Sampling Data Workflow Using Smtmk To Increase The Classification Accuracy Of Imbalanced Dataset