Effect of Window's Shape on Median Filtering

Appiah, Obed; Martey, Ezekiel Mensah; Quayson, Ebenezer

doi:10.1109/africon46755.2019.9133733

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The median filter with different window size gives low and high noise density values of MSE from RGB grey image and the image affected salt and pepper noise. The median performance is increased due to the increase in window size and then image gets blurring effect; it is decreased due to the increase of noise density [49]. Original MRI images are converted to gray scale filtered double class image, using median filter window sizes of 3, 5, 8, 10.…”

Section: Median Filter Methodsmentioning

confidence: 99%

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Jayanthi

Sivakumar

2022

AEJ

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Medical images have noise, such as salt and pepper, speckle, and gaussian noise. So, getting the accuracy of magnetic resonance images is challenging always. The accuracy of brain images is essential for the clinical diagnosis process. The nonlinear method of median and morphological filters is used for enhancement, contrast adjustment, and histogram equalization for medical images and speech processing, to preserve them from noise and edge features. Because of its edge-preserving properties, this median spatial filter window is critically important and better for removing impulsive noise and speckle noise. Nine different shapes of windows and five different sizes of windows and five noise density injections are used for evaluating the performance of the median filter. The first person J.W.Tukey, did the smoothing process by median rank selection filter. The denoising using morphological process is also considered for analysis. These two nonlinear methods like median filtering and morphological methods are used for the restoration of noise from the original image. These two methods are applied in normal and seizure-affected images to calculate quality assessment metrics and also the performance evaluation metrics from magnetic resonance images. Any image needs to be denoised to get the accuracy, precision, and F-measure kind of common feature evaluation metrics. This paper evaluates two nonlinear filtering methods and their performance evaluated based on shapes and size of windows and for suppressing or de-noising to produce original noise-free images to assist clinically.

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Section: Median Filter Methodsmentioning

confidence: 99%

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Jayanthi

Sivakumar

2022

AEJ

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“…Mean filter is used to transform images iteratively. The filtering, which employs a window size of n × n, is performed N times [34]. In each instance of the filtering process, the output of the previous filtering is used as input to that of the current filtering processes.…”

Section: Stacked Colour Histogram (Sch)mentioning

confidence: 99%

Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

Martey¹,

Lei

et al. 2021

IJCAI

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Image representation is critical to the successful realisation of Content-Based Image Retrieval (CBIR) systems. The choice of features to represent the image affects retrieval performance. Nowadays, image databases are heterogeneous and different feature types can be used for appropriate descriptions. This paper proposes an image representation for CBIR that combines Stacked Colour Histogram (SCH) and Conventional Colour Histogram (CCH) to improve image retrieval precision. This presented technique is designed to capture the colour and texture information of the image. The colour properties of an image are represented by CCH and that of texture by SCH. The weighted similarity measure is used to estimate the proportion of similarity values in the retrieval task. The novel descriptor has been widely tested on four standard image datasets, namely Batik, Coil100, Corel10K and Outext. Batik, Coil100 and Outext are used to assess texture discrimination. Corel10K is used to assess the discrimination of heterogeneous images. Experimental results and comparisons with SCH, CMTH, MTH, TCM, CTM and NRFUCTM demonstrate that the proposed descriptor has superior retrieval performance. Povzetek: V članku je podana učinkovita predstavitev slik z barvnim diagramom z namenom iskanja vsebine na slikah.

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Image Representation Using Stacked Colour Histogram

Martey

Lei

et al. 2021

Algorithms

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Image representation plays a vital role in the realisation of Content-Based Image Retrieval (CBIR) system. The representation is performed because pixel-by-pixel matching for image retrieval is impracticable as a result of the rigid nature of such an approach. In CBIR therefore, colour, shape and texture and other visual features are used to represent images for effective retrieval task. Among these visual features, the colour and texture are pretty remarkable in defining the content of the image. However, combining these features does not necessarily guarantee better retrieval accuracy due to image transformations such rotation, scaling, and translation that an image would have gone through. More so, concerns about feature vector representation taking ample memory space affect the running time of the retrieval task. To address these problems, we propose a new colour scheme called Stack Colour Histogram (SCH) which inherently extracts colour and neighbourhood information into a descriptor for indexing images. SCH performs recurrent mean filtering of the image to be indexed. The recurrent blurring in this proposed method works by repeatedly filtering (transforming) the image. The output of a transformation serves as the input for the next transformation, and in each case a histogram is generated. The histograms are summed up bin-by-bin and the resulted vector used to index the image. The image blurring process uses pixel’s neighbourhood information, making the proposed SCH exhibit the inherent textural information of the image that has been indexed. The SCH was extensively tested on the Coil100, Outext, Batik and Corel10K datasets. The Coil100, Outext, and Batik datasets are generally used to assess image texture descriptors, while Corel10K is used for heterogeneous descriptors. The experimental results show that our proposed descriptor significantly improves retrieval and classification rate when compared with (CMTH, MTH, TCM, CTM and NRFUCTM) which are the start-of-the-art descriptors for images with textural features.

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Effect of Window's Shape on Median Filtering

Cited by 3 publications

References 16 publications

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Image Enhancement and De-Noising Techniques of Magnetic Resonance Images

Effective Image Representation using Double Colour Histograms for Content-Based Image Retrieval

Image Representation Using Stacked Colour Histogram

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