Quality Analysis for Different Types of Noise Removal Filters

Digital Images are produced to record or display useful information. But due to imperfections in the imaging and capturing process, the recorded image invariably represents a degraded version of the original scene. The image enhancement is concerned with the manipulating an image so that the result is more suitable than the original for a specific application.  Enhancement techniques are used to remove the noise for better visualization [1]. Filtering a digital image to attenuate noise, while preserving the image details is an essential part of image processing. There are number of image enhancement filters such as spatial domain filters (linear filters, non-linear filters) & frequency domain filters (lowpass filters, highpass filters) which are used for upgrading the degraded image due to variety of noises. The different filters applied on an image produce different results. Different measures for different filters are used to assess the quality between the corresponding pixels in the original & the degraded image. In this research paper we applied & compare the effects of different frequency domain filters such as unsharp masking, homomorphic and adaptive wiener filter on bmp images. The quality index for these filters is evaluated based on seven quantitative measures (Average Difference, Maximum Difference, MSE, PSNR, NCC, Structural Content, Normalised Absolute Error) [7] [8].