An effective median filter for salt & pepper impulse range signals. An additive noise process may corrupt these noise removal is presented. This computationally efficient filtering digital images in both the acquisition and transmission stages. technique is implemented by a two pass algorithm: In the first Application-specific image filtering algorithms are needed to pass, identification of corrupted pixels that are to be filtered are simultaneously remove the effects of the corruptive process perfectly detected into a flag image using a variable sized detection window approach; In the second pass, using the and preserve important features of the images. Impulse noise detected flag image, the pixels to be modified are identified and removal in image processing often involves the removal of corrected by a more suitable median. Experimental results show these salt and pepper noises from images which is a very that the proposed algorithm performs far more superior than important pre-processing step for most other subsequent many of the median filtering techniques reported in terms of processing tasks such as edge detection, segmentation and retaining the fidelity of the image highly corrupted by impulse classification. In this area, early advances were dominated by noises even to the tune of ninety percent impulse noise. The linear filtering. They have had enormous impact on the proposed algorithm is free from patchy effects, does not extend lmelterf They techad formous stationary black or white blocks in the image as has been found in many development of various techniques for processing stationary other adaptive median based techniques and is very effective in and non-stationary signals. However, there are a large number cases when images are corrupted with large percentage of impulse of situations where linear filtering approach performs poorly. noises. This algorithm works very well for images with lower The limitation is the inability to simultaneously eradicate noise percentage of impulse noises also.
The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. There are two approaches to image fusion, namely Spatial Fusion and Transform fusion. In Spatial fusion, the pixel values from the source images are directly summed up and taken average to form the pixel of the composite image at that location. Transform fusion uses transform for representing the source images at multi scale. The most common widely used transform for image fusion at multi scale is Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance & poor directionality and these disadvantages are overcome by Stationary Wavelet Transform and Dual Tree Wavelet Transform. The conventional convolution-based implementation of the discrete wavelet transform has high computational and memory requirements. Lifting Wavelets has been developed to overcome these drawbacks. The Multi-Wavelet Transform of image signals produces a non-redundant image representation, which provides better spatial and spectral localization of image formation than discrete wavelet transform. And there are three levels of image fusion namely Pixel level, Area level and region level. This paper evaluates the performance of all levels of multi focused image fusion of using Discrete Wavelet Transform, Stationary Wavelet Transform, Lifting Wavelet Transform, Multi Wavelet Transform, Dual Tree Discrete Wavelet Transform and Dual Tree Complex Wavelet transform in terms of various performance measures.
The fast development of digital image processing leads to the growth of feature extraction of images which leads to the development of Image fusion. The process of combining two different images into a new single image by retaining salient features from each image with extended information content is known as Image fusion. Two approaches to image fusion are Spatial Fusion and Transform fusion. Discrete Wavelet Transform plays a vital role in image fusion since it minimizes structural distortions among the various other transforms. Lack of shift invariance, poor directional selectivity and the absence of phase information are the drawbacks of Discrete Wavelet Transform. These drawbacks are overcome by Stationary Wavelet Transform and Dual Tree Complex Wavelet Transform. This paper describes the optimal decomposition level of Discrete, Stationary and Dual Tree Complex wavelet transform required for better pixel based fusion of multi focused images in terms of Root Mean Square Error, Peak Signal to Noise Ratio and Quality Index
Automatic License Plate Recognition system (ALPR) is essential to implement law enforcement and traffic control on transportation systems. ALPR systems consist of the tasks: license plate localization, character segmentation, and character recognition; due to the positioning of the vehicles, the localized license plate images are mostly skewed which need to be de-skewed before proceeding to character segmentation. In this paper, a novel Skew Correction Algorithm is proposed focusing on boundary line that optimizes speed and accuracy by using the Hough transforms to get the skew corrected License plate image.
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