Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter

The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.

Design of Decision Based Recursive Weighted Median Filter With Exponential Weights

The prescribed algorithm for removing impulse noise effectively even under high noise densities without causing any loss of image details. Hence a cascaded section of median filters that, involves an Decision-based Median Filter followed by a Recursive Weighted Median (RWM) Filter employing exponential weights are used. The median controlled algorithm is employed to calculate the exponential weights. In the algorithms that where proposed in earlier which involves a cascaded section of the median with the RWM filters provided lesser Peak Signal/Noise Ratio (PSNR) and greater Mean Square Error(MSE) values. Hence the output appeared to be distorted for higher noise levels. These drawbacks have been eliminated in this proposed algorithm.

Development of Optimized Algorithm and Field Programmable Gate Array Implementation for Bio Medical Image Denoising for Health Informatics Applications

Denoising images is a most difficult task in applications for image processing. The image specifics are preserved and the additional sounds found in the images are removed. It is also a challenge to remove noise from medical and satellite images. It improves the diagnostic capacity of medical images and satellite images visual clarity. The noise in the images varies and its density varies depending on imaging techniques. The algorithms in the literature were suggested based on the noise density and the forms of noise. The aim of this paper is to eliminate the noise from ultrasound, magnetic resonance images and satellite images using an effective denoisation algorithm Hybrid Wiener Adaptive Weighted Median filter (HWAWMF) which is the combination of Wiener and Adaptive Centre Pixel Weighted Median Filter (ACPWMEF). In terms of performance parameters with an improved Peak to Signal Noise Ratio(PSNR), the hybrid filter shows better results than ACPWMEF. The Vienna filter takes out the additional noises in the images thus blurs the image’s optical perception. And also uses optimization approaches to enhance the image consistency. This paper proposes HWAWMF (PSO HWAWMF) based on particle swarm optimization and HWAWMF based on dragonfly optimization algorithms (DOAF HWAWMF). Visual vision and PSNR also have been improved by using the optimising algorithm at an average of 3.18 db, 4.83 db, and 3.14 db for lower noise (0.0% to 30%), medium noise (40% to 60%) as well as high noise density (70% to 90%). The efficacy of the algorithm using MATLAB R2013 is verified through both medical images, simulated and actual. In order to assess the computer complexity of the Altera algorithm for location, power and time using Cyclone II EP2C35F672C6, Cyclone II and Stratix III EP3SL150F1152C2, this algorithm is also implemented in the Altera Field Programable Gate Array (FPGA).

Modified Filter Equation with Improved Fuzzy Logic System Based Directional Median Filter for Mixed Noise

DWM (Directional weighted median) filter is very popular in filtering digital image and remove mixed noise. Fuzzy logic is implemented with median filters to improve its performance. In the previous work, fuzzy logic system is implemented with switching median filter and gives better performance than directional median filter as well as switching median filter. Experimenting directional median filter with same fuzzy logic system didn’t yield to better results therefore fuzzy logic parameters has been changes as per strong points of directional weighted median filter and a constant has been included in the filtering equation to improve the results. So in this proposed work, we have successfully implemented directional weighted median filter with fuzzy logic system which is proving better results than DWM and FSMF (Fuzzy Switching Median Filter). PSNR (Peak Signal to Noise Ratio)is used for qualitative analysis of results.

Find Outliers of Image Edge Consistency by Weighted Local Linear Regression with Equality Constraints

In this paper, we propose a general method to detect outliers from contaminated estimates of various image estimation applications. The method does not require any prior knowledge about the purpose, theory or hardware of the application but simply relies on the law of edge consistency between sources and estimates. The method is termed as ALRe (anchored linear residual) because it is based on the residual of weighted local linear regression with an equality constraint exerted on the measured pixel. Given a pair of source and contaminated estimate, ALRe offers per-pixel outlier likelihoods, which can be used to compose the data weights of post-refinement algorithms, improving the quality of refined estimate. ALRe has the features of asymmetry, no false positive and linear complexity. Its effectiveness is verified on four applications, four post-refinement algorithms and three datasets. It demonstrates that, with the help of ALRe, refined estimates are better in the aspects of both quality and edge consistency. The results are even comparable to model-based and hardware-based methods. Accuracy comparison on synthetic images shows that ALRe could detect outliers reliably. It is as effective as the mainstream weighted median filter at spike detection and is significantly better at bad region detection.

Proposed De-noising Algorithm

minimizing noises from images to restore it and increase its quality is a crucial step. For this, an efficient algorithms were proposed to remove noises such as (salt pepper, Gaussian, and speckle) noises from grayscale images. The algorithm did that by selecting a window measuring 3x3 as the center of processing pixels, other algorithms did that by using median filter (MF), adopted median filter (AMF), adopted weighted filter (AWF), and the adopted weighted median filter (AWMF). The results showed that the proposed algorithm compares to previous algorithms by having a better signal-to-noise ratio (PSNR).

A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification

The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the advantages of each data, hence benefiting accurate land cover classification. However, some current image fusion methods face the challenge of producing unexpected noise. To overcome the aforementioned problem, this paper proposes a novel fusion method based on weighted median filter and Gram–Schmidt transform. In the proposed method, Sentinel-2A images and GF-3 images are respectively subjected to different preprocessing processes. Since weighted median filter does not strongly blur edges while filtering, it is applied to Sentinel-2A images for reducing noise. The processed Sentinel images are then transformed by Gram–Schmidt with GF-3 images. Two popular methods, principal component analysis method and traditional Gram–Schmidt transform, are used as the comparison methods in the experiment. In addition, random forest, a powerful ensemble model, is adopted as the land cover classifier due to its fast training speed and excellent classification performance. The overall accuracy, Kappa coefficient and classification map of the random forest are used as the evaluation criteria of the fusion method. Experiments conducted on five datasets demonstrate the superiority of the proposed method in both objective metrics and visual impressions. The experimental results indicate that the proposed method can improve the overall accuracy by up to 5% compared to using the original Sentinel-2A and has the potential to improve the satellite-based land cover classification accuracy.