Terms Of Objective Quality Measures Research Articles

Wiener Filtering in Frequency Domain to Enhance Speech Corrupted by Colored Noise

This paper presents a method for speech enhancement to predict speech quality in presence of highly non-stationary scenarios using basic wiener filtering in frequency domain with an adaptive gain function under eight different noises at three different ranges of input SNR. Its performance is evaluated in terms of objective quality measures like LPC based spectral distortion measures are Cepstrum Distance, Itakura Saito and Log Likelihood Ratio. This method was tested using Noizeous database, its performance measures were compared against spectral subtractive type algorithms and it shows its improvements in terms of objective quality measures.

Adaptive Partition-Cluster-Based Median Filter for Random-Valued Impulse Noise Removal

As the most popular nonlinear denoise technique, the median filter has attracted significant attention in recent years. In this paper, a novel adaptive median filter is presented to remove random-valued impulse noise in images, named Adaptive Partition-Cluster-Based Median (APCM) Filter. Based on the partition cluster idea, the noise detector classifies pixels into different groups and identifies the noisy pixels in different regions adaptively without iterations. According to the results of noise detection, an improved adaptive decision-based filter is presented to restore the pixels which are corrupted by random-valued impulse noise. The proposed filter technique is open to any impulse noise. Extensive simulation results demonstrate that the proposed method substantially outperforms other state-of-the-arts impulse noise filter techniques both visually and in terms of objective quality measures. Furthermore, the proposed method is much friendly to the hardware parallel implementation of image processing because of its low computation complexity and simple realizable structure.

Using multiple frequency bins for stabilization of FD-ICA algorithms

In the frequency domain independent component analysis approaches for audio sources separation, the convolutive mixing problem is replaced by the solution of several instantaneous mixing problems, one for each frequency bin of the short time Fourier transform. This methodology yields good results but requires the solution of the permutation ambiguity. Moreover, the performance of the separation algorithms for each bin is not guaranteed to be equivalent, thus some bins can have worse results than others. In this paper a technique based on data from multiple bins is proposed to address these issues. The use of multiple bin information produces a coupling of the separation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing the computational cost. This can be mitigated by a sub-sampling of the multiple bins information. The results show that both approaches are beneficial for the frequency domain ICA approach, producing better separation in terms of objective quality measures.

REMOVAL OF GAUSSIAN AND IMPULSE NOISE IN THE COLOUR IMAGE PROGRESSION WITH FUZZY FILTERS

This paper is concerned with algebraic features based filtering technique, named as the adaptive statistical quality based filtering technique (ASQFT), is presented for removal of Impulse and Gaussian noise in corrupted colour images. A combination of these two filters also helps in eliminating a mixture of these two noises. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian impulse noise. The experiments shows that proposed method outperforms novel modern filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peaksignal- to-noise ratio (PSNR) and the normalized color difference (NCD). The expectations filter achieves a promising performance.

Cross Pattern Coherence Algorithm for Spatial Filtering Applications Utilizing Microphone Arrays

A parametric spatial filtering algorithm with a fixed beam direction is proposed in this paper. The algorithm utilizes the normalized cross-spectral density between signals from microphones of different orders as a criterion for focusing in specific directions. The correlation between microphone signals is estimated in the time-frequency domain. A post-filter is calculated from a multichannel input and is used to assign attenuation values to a coincidentally captured audio signal. The proposed algorithm is simple to implement and offers the capability of coping with interfering sources at different azimuthal locations with or without the presence of diffuse sound. It is implemented by using directional microphones placed in the same look direction and have the same magnitude and phase response. Experiments are conducted with simulated and real microphone arrays employing the proposed post-filter and compared to previous coherence-based approaches, such as the McCowan post-filter. A significant improvement is demonstrated in terms of objective quality measures. Formal listening tests conducted to assess the audibility of artifacts of the proposed algorithm in real acoustical scenarios show that no annoying artifacts existed with certain spectral floor values. Examples of the proposed algorithm can be found online at http://www.acoustics.hut.fi/projects/cropac/soundExamples.

An IHS-Based Fusion for Color Distortion Reduction and Vegetation Enhancement in IKONOS Imagery

In IKONOS imagery, both multispectral (MS) and panchromatic data are provided, with different spatial resolutions. IKONOS images can be fused or pan-sharpened for the visual interpretation of large-area-scale applications. To date, many image fusion techniques have been developed. In particular, the intensity-hue-saturation (IHS)-based methods are well known in quick image pan-sharpening. However, most of these processes produce color distortion due to the unnatural spectral response of IKONOS sensors, particularly in vegetated areas. Hence, many recent methods make use of vegetation enhancement in order to correct the unnatural color appearance. Generally, this enhancement is accomplished using vegetation indexes (VIs). A new fusion approach that produces images with natural colors is proposed. Moreover, in this technique, a high-resolution normalized difference VI is also proposed and used in delineating the vegetation. The procedure is performed in two steps: MS fusion using the IHS technique and vegetation enhancement. Vegetation enhancement is a correction step, and it depends on the considered application. The new approach provides very good results in terms of objective quality measures. In addition, visual analysis proves that the concept of the proposed approach is promising, and it improves well the fusion quality by enhancing the vegetated zones.

A Practical CFA Interpolation Using Local Map

This paper introduces a practical color filter array (CFA) interpolation technique. Among the many technologies proposed in this field, the inter-color methods that exploit correlation between color planes generally outperform the intra-color approaches. We have found that the filtering direction, e.g., horizontal or vertical, is among the most decisive factors for the performance of the CFA interpolation. However, most of the state-of-the-art technologies are not flexible enough in determining the filtering direction. For example, filtering only in the upper direction is not usually supported. In this context, we propose an inter-color CFA interpolation using a local map called unified geometry map (UGM). In this method, the filtering direction is determined based on the similarity of the local map data. Thus, it provides more choices of the filtering directions, which enhances the probability of finding the most appropriate direction. It is confirmed through simulations that the proposal outperforms the state-of-the-art algorithms in terms of objective quality measures. In addition, the proposed scheme is as inexpensive as the conventional methods with regard to resource consumption.