Denoising of impulse noise using partition-supported median, interpolation and DWT in dental X-ray images

The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images

Noise Filtering dengan Soft Weighted Median Filter untuk Meningkatkan Kualitas Segmentasi Citra

Salah satu faktor penghambat pada proses pengolahan citra adalah noise. Noise pada citra dibedakan menjadi dua jenis yaitu fixed-valued noise (salt & pepper noise) dan random-valued noise (gaussian, poisson, speckle, dan locarvar noise). Penelitian-penelitian sebelumnya yang terkait dengan noise filtering lebih fokus pada fixed-valued noise, sedangkan untuk random-valued noise masih jarang dilakukan. Penelitian ini mengusulkan metode Soft Weighted Median Filter (SWMF) untuk menghilangkan fixed-valued maupun random-valued noise. Untuk setiap piksel pada citra, langkah pertama yang dilakukan adalah menentukan window 3×3 untuk mencari piksel center dan piksel tetangganya. Kemudian semua nilai piksel pada window tersebut diurutkan dan dibagi menjadi tiga bagian, jika nilai piksel center berada pada bagian kedua, maka dianggap sebagai piksel bebas noise, sedangkan jika nilai piksel center berada pada bagian pertama atau bagian ketiga, maka dianggap sebagai piksel ber-noise. Langkah terakhir pada proses ini adalah mengganti nilai piksel ber-noise dengan nilai rata-rata median tertimbang dari semua piksel dalam window, sedangkan piksel bukan noise dibiarkan tidak berubah. Nilai piksel baru dari proses ini digunakan kembali untuk perhitungan berikutnya. Citra hasil dari metode SWMF dibandingkan dengan metode-metode yang lain seperti; Median Filter, Mean Filter, Wiener Filter dan Gaussian Filter lewat pengukuran Mean Squared Error (MSE) dan Peak Signal to Noise Ratio (PSNR). Proses segmentasi citra dilakukan pada citra hasil noise filtering, terdiri dari 2 proses yaitu deteksi area (Top-Hat Transform) dan deteksi garis (Sobel Edge Detection). Analisis kinerja pada tahap ini menggunakan perhitungan sensitivity, specificity, dan accuracy antara citra groundtruth dengan citra hasil segmentasi. Berdasarkan hasil uji coba, dapat disimpulkan bahwa metode Soft Weighted Median Filter berhasil meningkatkan kualitas segmentasi citra dengan cara menghilangkan menghilangkan fixed-valued maupun random-valued noise, metode ini memiliki rata-rata nilai PSNR paling tinggi dibandingkan metode lainnya yaitu sebesar 29,21 db. ========================================================================================================== One of the inhibiting factors in image digital processing is noise. Noise in the image is divided into two types: fixed-valued noise (salt & pepper noise) and random-valued noise (gaussian, poisson, speckle, and locarvar noise). Previous studies of noise filtering focus on fixed-valued noise, while random-valued noise is rarely done. This research proposes a Soft Weighted Median Filter (SWMF) method to remove fixed-valued dan random-valued noise. For each pixel in the image, the first step is determine the 3×3 window to search the center pixel and neighboring pixels. Then all pixel values in the window are sorted and divided into three parts, if the the center pixel value in the part two, it is considered as noise-free pixel, whereas if the center pixel value in part one or part three, it is considered as noise pixel. The final step in this process is replace the noise-pixel value with the average of median weighted value of all pixels in the window, while the noise-free pixel are left unchanged. The new pixel value from this process is reused for the next pixel calculation. The result images of the SWMF method are compared with other methods such as: Median Filter, Mean Filter, Wiener Filter and Gaussian Filter with the measurement of Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). Image segmentation process is done on the image of noise filtering result. There are two image segmentation process, firstly, area detection using Top-Hat Transform, and secondly, line detection using Sobel Edge Detection. Performance analysis at this stage using the calculation of sensitivity, specificity, and accuracy between groundtruth images with the image of the results of segmentation. Based on the experiment results, Soft Weighted Median Filter method succeeded to improve the quality of image segmentation by eliminating fixed-valued and random-valued noise. This method has the highest average PSNR value compared to other methods of 29.21 db

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level