Removing outliers from the Lucas-Kanade method with a weighted median filter

Master's thesis in Automation and signal processingThe definition of optical flow is stated as a brightness pattern of apparent motion of objects, through surfaces and edges in a visual scene. This technique is used in motion detection and segmentation, video compression and robot navigation. The Lucas-Kanade method uses information from the image structure to compose a gradient based solution to estimate velocities, also known as movement of X- and Y-direction in a scene. The goal is to obtain an accurate pixel motion from an image sequence The objective of this thesis is to implement a post processing step with a weighted median lter to a well known optical flow method; the Lucas-Kanade. The purpose is to use the weighted median lter to remove outliers, vectors that are lost due to illumination changes and partial occlusions. The median filer will replace velocities that are under represented in neighbourhoods. A moving object will have corners not just edges, and these vectors have to be preserved. A weighted median filter is introduced to ensure that the under represented vectors is preserved. Error is measured through angular and endpoint error, describing accuracy of the vector field. The iterative and hierarchical LK method have been studied. The iterative estimation struggles less with single error. Because of this the weighted median filter did not improve the iterative LK-method. The hierarchical estimation is improved by the weighted median and reduced the average error of both angular and endpoint error

A New Structure of Stereo Algorithm Using Pixel Based Differences and Weighted Median Filter

This paper proposed a new algorithm for stereo vision system to obtain depth map or disparity map. The proposed stereo vision algorithm consists of three stages, matching cost computation, disparity optimization and disparity refinement. The first stage starts with matching cost computation, where pixel based differences methods are used. The matching methods are the combination of Absolute Difference (AD) and Gradient Matching (GM). Next, the second stage; disparity optimization utilizes Winner-Takes-All (WTA) technique to normalize the disparity values of each pixel of the image. Finally, for disparity refinement stage, weighted median (WM) filter is added to reduce and smother the noise on the disparity map

Optimum non linear binary image restoration through linear grey-scale operations

Non-linear image processing operators give excellent results in a number of image processing tasks such as restoration and object recognition. However they are frequently excluded from use in solutions because the system designer does not wish to introduce additional hardware or algorithms and because their design can appear to be ad hoc. In practice the median filter is often used though it is rarely optimal. This paper explains how various non-linear image processing operators may be implemented on a basic linear image processing system using only convolution and thresholding operations. The paper is aimed at image processing system developers wishing to include some non-linear processing operators without introducing additional system capabilities such as extra hardware components or software toolboxes. It may also be of benefit to the interested reader wishing to learn more about non-linear operators and alternative methods of design and implementation. The non-linear tools include various components of mathematical morphology, median and weighted median operators and various order statistic filters. As well as describing novel algorithms for implementation within a linear system the paper also explains how the optimum filter parameters may be estimated for a given image processing task. This novel approach is based on the weight monotonic property and is a direct rather than iterated method

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. © 2004 IEEE.In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the sec ond filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.This work was supported in part by CenSSIS, the Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (NSF) under Award EEC-9986821, by an ARO MURI on Demining under Grant DAAG55-97-1-0013, and by the NSF under Award 0208548

Speckle Reduction with Attenuation Compensation for Skin OCT Images Enhancement

The enhancement of skin image in optical coherence tomography (OCT) imaging can help dermatologists to investigate tissue layers more accurately, hence the more efficient diagnosis. In this paper, we propose an image enhancement technique including speckle reduction, attenuation compensation and cleaning to improve the quality of OCT skin images. A weighted median filter is designed to reduce the level of speckle noise while preserving the contrast. A novel border detection technique is designed to outline the main skin layers, stratum corneum, epidermis and dermis. A model of the light attenuation is then used to estimate the absorption coefficient of epidermis and dermis layers and compensate the brightness of the structures at deeper levels. The undesired part of the image is removed using a simple cleaning algorithm. The performance of the algorithm has been evaluated visually and numerically using the commonly used no-reference quality metrics. The results shows an improvement in the quality of the images. Keywords: Optical coherence tomography (OCT), Skin, Image enhancement, Speckle reduction, Attenuation compensation

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