Image Quality Assessment and Noise Reduction on Distinct Noise Images
DOI:
https://doi.org/10.32628/IJSRST207444Keywords:
Image Quality Assessment, Gaussian Noise, Salt and Pepper noise, Wiener filter and Shape Adaptive Discrete Cosine Transform.Abstract
Image quality assessment is a procedure of evaluating the quality of an image and in the past few years, the desire of image-based applications has grown hugely; therefore the significance of methodical and accurate assessment of the quality of the image is necessary. For many image processing applications assessing the quality of the image is elementary, whereas the purpose of image quality assessment (IQA) method are to automate the estimation of the quality of images in simultaneously with human quality perception. In this paper, we have explored a novel image quality algorithm that depicts the quality of an image using Wiener Filter and Shape Adaptive Discrete Cosine Transform.Here, we have applied two different noise levels using two noises, Gaussian noise and Salt and pepper noise. The input image is taken from public repository and applied Salt and pepper noise and Gaussian noise at two different levels. The applied noise is then reduced by using Wiener Filter and Shape Adaptive – Discrete Cosine Transform (SA-DCT). An experimental result shows the performance of different types of filters to denoise the noised images from different levels of noises in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Structural Similarity Index Metric (SSIM), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR).
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