Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.

A new technology for video frame rate up-conversion (FRUC) is presented by combining a median filter and motion estimation (ME) with an occlusion detection (OD) method. First, ME is performed to obtain a motion vector. Then, the OD method is used to refine the MV in the occlusion region. When occlusion occurs, median filtering is applied. Otherwise, bidirectional motion compensated interpolation (BDMC) is applied to create the interpolated frames. The experimental results show that the proposed algorithm provides better performance than the conventional approach. The average gain in the PSNR (Peak Signal to Noise Ratio) is always better than the other methods in the Full HD test sequences.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.

In this paper, we propose a new motion vector smoothing algorithm using weighted vector median filtering based on edge direction for frame interpolation. The proposed WVM (Weighted Vector Median) system adjusts the weighting values based on edge direction, which is derived from spatial coherence between the edge direction continuity of a moving object and motion vector (MV) reliability. The edge based weighting scheme removes the effect of outliers and irregular MVs from the MV smoothing process. Simulation results show that the proposed algorithm can correct wrong motion vectors and thus improve both the subjective and objective visual quality compared with conventional methods.

In this paper, we present a directional interpolation filter in which the minimum and maximum pixels in the given window are excluded. Image pixels within a predefined window are ranked and classified as minimum-maximum or exclusive level, and then passed through the interpolation and identity filters, respectively. Extensive simulations show that the proposed filter performs better than other nonlinear filters in preserving desired image features while reducing impulse noise effectively.

A new impulse noise detection algorithm is presented, which can successfully remove impulse noise from corrupted images while preserving image details. The impulse detection algorithm is combined with median filtering to achieve noise removal. The main advantage of the proposed algorithm is that it can detect the impulse noise with high accuracy while reducing the probability of detecting image details as impulses. Also, it can be applied iteratively to improve the quality of restored images. It is efficient and low in complexity. Furthermore, it requires no previous training. Extensive experimental results show that the proposed approach significantly outperforms many well-known techniques.

In this paper, we present a minimum-maximum exclusive weighted-mean filtering algorithm with adaptive window. Image pixels within the varying size of the window are ranked and classified as minimum-maximum and median levels, and then passed through the weighted-mean of median level and identity filters, respectively. The filtering window size is adaptively increasing according to noise ratio without noise measurement. Extensive simulations show that the proposed filter performs better than other median/rank-type filters in removing impulse noise of highly corrupted images.

A new sorting algorithm and architecture for fast median filter are proposed. This algorithm results in low area VLSI architecture producing low switching activity and without using feedback. The main idea is to employ the extra matrix for fast search operation of rank of oldest window element. We simulated and synthesized this algorithm using SYNOPSYSTM and showed the sufficiency in real time operation.

A novel effective method for detection and removal impulse noise in highly corrupted color images is proposed. This detection-estimation method consists of two steps. Outliers are first detected using spatial relations between the color components. Then the detected noise pixels are replaced with the output of the vector median filter over a local spatially connected area excluding the outliers. Simulation results in a test color image show a superior performance of the proposed filtering algorithm comparing to the conventional vector median filter. The comparisons are made using a mean square error and a mean absolute error criteria.

In this study, we consider a filtering method for image sequence degraded by additive Gaussian noise and/or impulse noise (i.e., mixed noise). For removing the mixed noise from the 1D/2D signal, weighted median filters are well known as a proper choice. We have also proposed a filtering tool based on the weighted median filter with a data-dependent method. We call this data-dependent weighted median (DDWM) filters. Nevertheless, the DDWM filter, its weights are controlled by some local information, is not enough performance to restore the image sequence degraded by the noise. The reason is that the DDWM filter is not able to obtain good filtering performance both in the still and moving regions of an image sequence. To overcome above drawback, we add motion information as a motion detector to the local information that controls the weights of the filters. This new filter is proposed as a Video-Data Dependent Weighted Median (Video-DDWM) filter. Through some simulations, the Video-DDWM filter is shown to give effective restoration results than that given by the DDWM filtering and the conventional filtering method with a motion-conpensation (MC).

In this paper, we propose a fast algorithm to realize parallel median filter for processing 1-D and 2-D signal. In the proposed pipelined architecture, m-passes are employed for filtering signal while word resolution is m bits. One pass employs one processing element (PE), and the number of PEs is independent of the number of samples. Therefore, we only need m PEs for real-time operation. With 8-bits resolution, the system gate-count is less than 5 k. Moreover, this median architecture could be easily modified to consist of the programmable feature that may choose the better sampling number to filter signal. It should be also noted that our proposed processing flow has a progressive property, which is very suitable for bandwidth-limited channel application.

Visualization of 3-D ultrasound images is a challenging task due to the noisy and fuzzy nature of ultrasound imaging. This paper presents an efficient volume rendering technique for 3-D ultrasound image. A preprocessing technique of 2-D truncated-median filtering is proposed to reduce speckle noise of the ultrasound image. This paper also introduces an adaptive boundary detection method to reduce the computation time for volume rendering of ultrasound image. The proposed technique is compared to the conventional volume rendering methods with respect to the computation time and the subjective image quality. According to the comparison study, the proposed volume rendering method shows good performance for visualization of 3-D ultrasound image.

A new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multispectral image processing is introduced and applied to color image filtering problems. These filters are based on Rational Functions (RF). The VMRHF filter is a two-stage filter, which exploits the features of the vector median filter and those of the rational operator. The filter output is a result of vector rational function operating on the output of three sub-functions. Two vector median (VMF) sub-filters and one center weighted vector median filter (CWVMF) are proposed to be used here due to their desirable properties, such as, edge and details preservation and accurate chromaticity estimation. Experimental results show that the new VMRHF outperforms a number of widely known nonlinear filters for multispectral image processing such as the Vector Median ilter (VMF) and Distance Directional Filters (DDf) with respect to all criteria used.

In this paper, quasi-Gaussian filter, quasi-median filter and locally adaptive filters are introduced. A new adaptive vector filter based on noise estimate is proposed to suppress Gaussian and/or impulse noise. To estimate the type and degree of noise corruption, a noise detector and an edge detector are introduced, and two key parameters are obtained to characterize noise in color image. After globally estimating the type and degree of noise corruption, different locally adaptive filters are properly chosen for image enhancement. All noisy images, used to test filters in experiments, are generated by PaintShopPro and Photoshop software. Experimental results show that the new adaptive filter performs better in suppressing noise and preserving details than the filter in Photoshop software and other filters.

We propose a robust detection scheme by employing an order statistic filter as a preprocessor of the input signal. For ease of design, the variance of the order statistic filtered output is modeled by proposing an approximate upper bound. The detector is analytically designed using a fixed sample size (FSS) test scheme. The performance of the proposed detector is compared to that of other robust detectors in terms of the sample size required for given false alarm and miss detection probabilities. Finally, analytical results are verified by computer simulation.

An analog circuit is devised which selects and outputs the kth largest element among n input voltages. The circuit is composed of n basic transconductance amplifiers connected mutually with an O(n) length wire, thus the complexity of the circuit is O(n). The circuit becomes particularly simple for the case of the selection of the median of inputs.

A two-cascaded image processing approach to enhance the subtle differences in X-ray CT image is proposed. In the method, an asymmetrical non-linear subfilter is introduced to reduce the noise inherent in the image while preserving local edges and directional structural information. Then, a subfilter is used to compress the global dynamic range of the image and emphasize the details in the homogeneous regions by performing a modular transformation on local image den-sities. The modular transformation is based on a dynamically defined contrast fator and the histogram distributions of the image. The local contrast factor is described in accordance with Weber's fraction by a two-layer neighborhood system where the relative variances of the medians for eight directions are computed. This method is suitable for low contrast images with wide dynamic ranges. Experiments on X-ray CT images of the head show the validity of the method.

The purpose of our research is to get further improvement in the performance of order statistic filters. The basic idea found in our research is the use of a robust median estimator to obtain median differential order information which the classes of order statistic filter required in order to sort the input signal in the filter window. In order to give the motivation for using a median estimator in the classes of order statistic filters, we derive theorems characterizing the median filters and prove them theoretically using the characteristic that the order statistic filter has the performance for a monotonic signal equivalent with the FIR linear filter. As an application of median operation, we propose and investigate the Median Differential Order Statistic Filter to reduce impulsive noise as well as Gaussian noise and regard it as a subclass of the Order Statistic Filter. Moreover, we introduce the piecewise linear function in the Median Differential Order Statistic Filter to improve performance in terms of edge preservation. We call it the Piecewise Linear Median Differential Order Statistic Filter. The effectiveness of proposed filters is verified theoretically by computing the output Mean Square Error of the filters in parts of edge signals, impulsive noise, small amplitude noise and their combination. Computer simulations also show that the proposed filter can improve the performance in both noise (small-amplitude Gaussian noise and impulsive noise) reduction and edge preservation for one-dimensional signals.