Variable pixel G-neighbor filters

Yerbol Akhmetov, Joshin John Mathew, Alex Pappachen James

2017

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DOI 10.1109/iscas.2017.8050772
Data confidence 85 high
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Abstract

Main challenge with denoising applications using conventional image filtering with fixed size windows is the smearing or blurring of various region boundaries. This blurring is the visual representation of deviation in estimated denoised value (region mean) due to the presence of heterogeneous (or dissimilar pixels) regions in region-averaging along with the homogenous (or similar pixels) regions. Thus an adaptive window shape selection using only the most similar pixels (G-Neighbor) within the fixed window can avoid the presence of such undesired heterogeneous regions from mean intensity calculation. This Variable Pixel G-Neighbor filter is implemented using CMOS circuits and further simulated in MATLAB. This analog hardware approach demonstrates the possibilities in visual quality enhancement in image acquisition stages itself. Also the near-real time response of this pre-processor offers a practical solution to image computing problems caused by image quality and processing resources limitations. The improvement in image quality is evaluated by comparing results from conventional Mean Filter and its modified version using Variable Pixel G-Neighbor Filter. Metrics evaluates the signal strength enhancement (Peak Signal-to-Noise Ratio, PSNR), amount of noise removal (Mean Square Error, MSE), and structural preservation which indicates minimal deformation or blurring (Structural Similarity Index Measure, SSIM). From sample result it is shown that proposed approach offers PSNR = 41.25, MSE = 3.9, SSIM = 0.85 against conventional mean filter having values 38.64, 7.4, and 0.71 respectively.