Effect of kernel size on Wiener and Gaussian image filtering

In this paper, the effect of the kernel size of Wiener and Gaussian filters on their image restoration qualities has been studied and analyzed. Four sizes of such kernels, namely 3x3, 5x5, 7x7 and 9x9 were simulated. Two different types of noise with zero mean and several variances have been used: Gaussian noise and speckle noise. Several image quality measuring indices have been applied in the computer simulations. In particular, mean absolute error (MAE), mean square error (MSE) and structural similarity (SSIM) index were used. Many images were tested in the simulations; however the results of three of them are shown in this paper. The results show that the Gaussian filter has a superior performance over the Wiener filter for all values of Gaussian and speckle noise variances mainly as it uses the smallest kernel size. To obtain a similar performance in Wiener filtering, a larger kernel size is required which produces much more blur in the output mage. The Wiener filter shows poor performance using the smallest kernel size (3x3) while the Gaussian filter shows the best results in such case. With the Gaussian filter being used, similar results of those obtained with low noise could be obtained in the case of high noise variance but with a higher kernel size

Image Enhancement for Scanned Historical Documents in the Presence of Multiple Degradations

Historical documents are treasured sources of information but typically suffer from problems with quality and degradation. Scanned images of historical documents suffer from difficulties due to paper quality and poor image capture, producing images with low contrast, smeared ink, bleed-through and uneven illumination. This PhD thesis proposes a novel adaptative histogram matching method to remove these artefacts from scanned images of historical documents. The adaptive histogram matching is modelled to create an ideal histogram by dividing the histogram using its Otsu level and applying Gaussian distributions to each segment with iterative output refinement applied to individual images. The pre-processing techniques of contrast stretching, wiener filtering, and bilateral filtering are used before the proposed adaptive histogram matching approach to maximise the dynamic range and reduce noise. The goal is to better represent document images and improve readability and the source images for Optical Character Recognition (OCR). Unlike other enhancement methods designed for single artefacts, the proposed method enhances multiple (low-contrast, smeared-ink, bleed-through and uneven illumination). In addition to developing an algorithm for historical document enhancement, the research also contributes a new dataset of scanned historical newspapers (an annotated subset of the Europeana Newspaper - ENP – dataset) where the enhancement technique is tested, which can also be used for further research. Experimental results show that the proposed method significantly reduces background noise and improves image quality on multiple artefacts compared to other enhancement methods. Several performance criteria are utilised to evaluate the proposed method’s efficiency. These include Signal to Noise Ratio (SNR), Mean opinion score (MOS), and visual document image quality assessment (VDIQA) metric called Visual Document Image Quality Assessment Metric (VDQAM). Additional assessment criteria to measure post-processing binarization quality are also discussed with enhanced results based on the Peak signal-to-noise ratio (PSNR), negative rate metric (NRM) and F-measure.Keywords: Image Enhancement, Historical Documents, OCR, Digitisation, Adaptive histogram matchin

Modified Spatially Adaptive Denoising Algorithm for an Image Corrupted by Gaussian Noise

Abstract: A modified spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise is proposed in this paper. The proposed algorithm use local statistics of a selected window i.e. by defining local weighted mean, local weighted activity and local maximum. These local statistics are used to detect the noise in the image then a modified Gaussian filter is used for noise suppression. This algorithm is tested against different images and the experimental result shows its result is better than different existing methods like Pixel Wise Median Absolute Difference (PWMAD), Rank Order Criteria (ROC), Switching-based Adaptive Weighted Mean (SAWM) and Spatially Adaptive Denoising Algorithm (SADA)