2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

Real-time filtering and detection of dynamics for compression of HDTV

The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals

Development of image restoration techniques

Image denoising and image deblurring are studied as part of the thesis. In deblurring, blind deconvolution is investigated. Out of the several classes of blind deconvolution techniques, Non parametric Methods based on Image Constraints are studied at greater depth. A new algorithm based on the Iterative Blind Deconvolution(IBD) technique is developed. The algorithm makes use of spatial domain constraints of non-negativity and support. The Fourier-domain constraint may be described as constraining the product of the Fourier spectra of the image f and the Fourier spectra of the point spread function h to be equal to the convolution spectrum. Within each iteration, the algorithm switches between spatial domain and frequency domain and imposes known constraints on each. The convergence of the original IBD can be accelerated by limiting high magnitude values in frequency domain of both estimated image and point spread function. The new algorithm converges within less than 25 iterations where as the original IBD took nearly 500 iterations. Inclusion of the support constraint in the spatial domain improves quality of the restored image. Also, sum of the spatial domain values of the point spread function should be made equal to one at the end of each iteration, for preventing the loss of image intensity. PSNR values calculated for restored images show signi¯cant improvement in image quality. A PSNR of 17.8dB is obtained for the improved scheme where as it is 14.3dB for the original IBD. A new stopping criteria based on standard deviation of the image power for last k iterations is de¯ned for stopping the algorithm when it converges. In denoising, an edge retrieval technique is developed which preserves the image details along with e®ectively removing impulse noise. Noisy pixels are detected in the ¯rst phase and in the next phase those pixel values are replaced with an estimate of the actual value. For dealing with the wrong classi¯cation of edge pixels as noisy pixels, an edge retrieval technique based on pixel-wise MAD is de¯ned. This scheme retrieves the pixels which are wrongly classi¯ed as noise. The algorithm gives high PSNR values as well as very good detail preservation

Sorted Min-Max-Mean Filter for Removal of High Density Impulse Noise

This paper presents an improved Sorted-Min-Max-Mean Filter (SM3F) algorithm for detection and removal of impulse noise from highly corrupted image. This method uses a single algorithm for detection and removal of impulse noise. Identification of the corrupted pixels is performed by local extrema intensity in grayscale range and these corrupted pixels are removed from the image by applying SM3F operation. The uncorrupted pixels retain its value while corrupted pixel’s value will be changed by the mean value of noise-free pixels present within the selected window. Different images have been used to test the proposed method and it has been found better outcomes in terms of both quantitative measures and visual perception. For quantitative study of algorithm performance, Mean Square Error (MSE), Peak-Signal-to-Noise Ratio (PSNR) and image enhancement factor (IEF) have been used. Experimental observations show that the presented technique effectively removes high density impulse noise and also keeps the originality of pixel’s value. The performance of proposed filter is tested by varying noise density from 10% to 90% and it is observed that for impulse noise having 90% noise density, the maximum PSNR value of 30.03 dB has been achieved indicating better performance of the SM3F algorithm even at 90% noise level. The proposed filter is simple and can be used for grayscale as well as color images for image restoration

Partition based vector filtering technique for suppression of noise in digital color images

A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity

Segmentation-assisted detection of dirt impairments in archived film sequences

A novel segmentation-assisted method for film dirt detection is proposed. We exploit the fact that film dirt manifests in the spatial domain as a cluster of connected pixels whose intensity differs substantially from that of its neighborhood and we employ a segmentation-based approach to identify this type of structure. A key feature of our approach is the computation of a measure of confidence attached to detected dirt regions which can be utilized for performance fine tuning. Another important feature of our algorithm is the avoidance of the computational complexity associated with motion estimation. Our experimental framework benefits from the availability of manually derived as well as objective ground truth data obtained using infrared scanning. Our results demonstrate that the proposed method compares favorably with standard spatial, temporal and multistage median filtering approaches and provides efficient and robust detection for a wide variety of test material

Investigation of image enhancement techniques for the development of a self-contained airborne radar navigation system

This study was devoted to an investigation of the feasibility of applying advanced image processing techniques to enhance radar image characteristics that are pertinent to the pilot's navigation and guidance task. Millimeter (95 GHz) wave radar images for the overwater (i.e., offshore oil rigs) and overland (Heliport) scenario were used as a data base. The purpose of the study was to determine the applicability of image enhancement and scene analysis algorithms to detect and improve target characteristics (i.e., manmade objects such as buildings, parking lots, cars, roads, helicopters, towers, landing pads, etc.) that would be helpful to the pilot in determining his own position/orientation with respect to the outside world and assist him in the navigation task. Results of this study show that significant improvements in the raw radar image may be obtained using two dimensional image processing algorithms. In the overwater case, it is possible to remove the ocean clutter by thresholding the image data, and furthermore to extract the target boundary as well as the tower and catwalk locations using noise cleaning (e.g., median filter) and edge detection (e.g., Sobel operator) algorithms

Partition-based vector filtering technique for suppression of noise in digital color images

Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe