Method to increase the contrast and reduce the noise of underwater image.Applied histogram modification of integrated RGB and HSV color models.Mapping the image histogram according to Rayleigh distribution.Limiting the dynamic range of color models to reduce under- and over-enhanced areas.Outperforms other state-of-the-art methods in term of contrast and noise reduction. The physical properties of water cause light-induced degradation of underwater images. Light rapidly loses intensity as it travels in water, depending on the color spectrum wavelength. Visible light is absorbed at the longest wavelength first. Red and blue are the most and least absorbed, respectively. Underwater images with low contrast are captured due to the degradation effects of light spectrum. Therefore, the valuable information from these images cannot be fully extracted for further processing. The current study proposes a new method to improve the contrast and reduce the noise of underwater images. The proposed method integrates the modification of image histogram into two main color models, Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV). In the RGB color model, the histogram of the dominant color channel (i.e., blue channel) is stretched toward the lower level, with a maximum limit of 95%, whereas the inferior color channel (i.e., red channel) is stretched toward the upper level, with a minimum limit of 5%. The color channel between the dominant and inferior color channels (i.e., green channel) is stretched to both directions within the whole dynamic range. All stretching processes in the RGB color model are shaped to follow the Rayleigh distribution. The image is converted into the HSV color model, wherein the S and V components are modified within the limit of 1% from the minimum and maximum values. Qualitative analysis reveals that the proposed method significantly enhances the image contrast, reduces the blue-green effect, and minimizes under- and over-enhanced areas in the output image. For quantitative analysis, the test with 300 underwater images shows that the proposed method produces average mean square error (MSE) and peak signal to noise ratio (PSNR) of 76.76 and 31.13, respectively, which outperform six state-of-the-art methods.

Underwater image quality enhancement through integrated color model with Rayleigh distribution

The effectiveness of a digital watermarking algorithm is indicated by the robustness of embedded watermarks against various attacks. Improving the robustness of a watermark so as to withstand attacks has been one of the main research objectives in digital image watermarking. The two issues of existing feature-based schemes that have to be addressed are: one is avoiding repeated selection of robust regions for watermarking to resist similar attacks, and the other is the difficulty of selecting the most robust and smallest feature region set to be watermarked. In order to achieve robustness, an overall architecture for a feature-based robust digital image watermarking scheme is designed. A simulated attacking procedure is performed using predefined attacks to evaluate the robustness of every candidate feature region selected. Comparing with some well-known feature-based methods, the proposed method exhibits better performance in robust digital watermarking.

A feature-based robust digital image watermarking scheme

Empirical Mode Decomposition for adaptive AM-FM analysis of Speech

This paper proposed an efficient algorithm for contrast enhancement of natural images. The contrast of images is very important characteristics by which the quality of images can be judged as good or poor. The proposed algorithm consists of two stages: In the first stage the poor quality of an image is processed by modified sigmoid function. In the second stage the output of the first stage is further processed by contrast limited adaptive histogram equalization to enhance contrast of images. In order to achieve better contrast enhancement of images, a novel mask based on input value together with the modified sigmoid formula that will be used as contrast enhancer in addition to contrast limited adaptive histogram equalization. This new contrast enhancement algorithm passes over the input image which operates on its pixels one by one in spatial domain. Simulation and experimental results on benchmark test images demonstrates that proposed algorithm provides better results as compared to other state*of*art contrast enhancement techniques. Proposed algorithm performs efficiently in different dark and bright images by adjusting their contrast very frequently. Proposed algorithm is very simple and efficient approach for contrast enhancement of image. This algorithm can be used in various applications where images are suffering from different contrast problems.

Efficient Algorithm for Contrast Enhancement of Natural Images

Perceptually inspired color correction methods are characterized by human visual system properties In this paper, we propose a perceptually inspired variational method for uneven intensity correction of remote sensing images The proposed method shares the same intrinsic scheme as the Retinex theory, but the reflectance in this method is solved directly within the limited dynamic range and is supposed to comply with the gray world assumption Considering the smoothness of illumination and the complexity of reflectance, the proposed method integrates L2 norm and total variation prior to inflict varying constraints to different components and regions The minimum of this variational model is calculated using the steepest descent approach Experimental results are presented to validate the effectiveness of the proposed method

A Perceptually Inspired Variational Method for the Uneven Intensity Correction of Remote Sensing Images

Histogram based techniques is one of the important digital image processing techniques which can be used for image enhancement. One of the advantages of histogram based techniques is simplicity of implementation of the algorithm. Also it should be mentioned that histogram based techniques is much less expensive comparing to the other methods. Histogram based techniques for image enhancement is mostly based on equalizing the histogram of the image and increasing the dynamic range corresponding to the image. Histogram equalization (HE) method has two main disadvantages which affect efficiency of this method. For solving the above problems, some techniques have proposed for example using bi histogram equalization (BHE) algorithm instead of histogram equalization (HE). It should be mentioned that bi histogram equalization (BHE) is one of the best proposed algorithm which has proposed until now. This paper presents a novel algorithm for contrast enhancement based on histogram equalization (HE). Our proposed algorithm applies some preprocessing steps on the histogram corresponding to the image and then applies histogram equalization. We have applied our proposed algorithm on a database which includes 220 normal images and results are promising. Our proposed method has better results comparing with bi histogram equalization (BHE) algorithm based on visual criterion and a mathematical criterion.

A novel approach for contrast enhancement based on Histogram Equalization

This paper presents a novel technique to increase the quality of medical images based on histogram equalization. In the proposed method first we have applied a noise reduction method and then we apply some suitable preprocessing on histogram of the medical images and after that histogram equalization has been applied on the new histogram. Our proposed method in despite of its simplicity has better results in compare to other usual methods based on histogram equalization. The quality of resulted images after applying our proposed methods has been tested on a database (medical images) with a confirmed criterion by viewer. Also we have considered a mathematical criterion for comparing our proposed algorithm with other available methods for contrast enhancement. Results show the better efficiency of the proposed method.

A Novel Approach for Contrast Enhancement in Biomedical Images Based on Histogram Equalization

Mel-frequency cepstral coefficients (MFCC) are the most widely used features for speech recognition. However, MFCC-based speech recognition performance degrades in presence of additive noise. In this paper, we propose a set of noise-robust features based on conventional MFCC feature extraction method. Our proposed method consists of two steps. In the first step, mel sub-band Wiener filtering is carried out. The second step consists of estimating SNR in each sub-band and calculating the sub-band entropy by defining a weight parameter based on sub-band SNR to entropy ratio. The weighting has been carried out in a way that gives more important roles, in cepstrum parameter formation, to sub-bands that are less affected by noise. Experimental results indicate that this method leads to improved ASR performance in noisy environments. Furthermore, due to the simplicity of the implementation of our method, its computational overhead in comparison to MFCC is quite small.

Weighting of Mel Sub-bands Based on SNR/Entropy for Robust ASR

In this paper, we present a new back-end classifier for GMM-LM based language identification systems. Our new proposed system consists of two main parts, mapping matrix and bank of SVMs. These two parts are located in series after GMM-LM system. The mapping matrix, maps the language models' output vectors to a new space in which the languages are more separable than before. Then each SVM in the SVM bank separates one language from the others. We used a new sequence kernel for each SVM in the bank. We show that our new sequence kernel-based SVMs separate languages more efficiently than common Gaussian mixture and GLDS SVM back-end classifiers. Also our new mapping matrix outperforms common linear discriminant matrix in separating classes from each other. Using these two parts increases the LID accuracy noticeably in comparison with the other LDA-GMM and LDA-GLDS SVM back-end classifiers. Our experiments on 5 languages from OGI-TS multilanguage task, prove our claim.

Spoken Language Identification Using a New Sequence Kernel-based SVM Back-end Classifier

The Mel-frequency cepstral coefficients (MFCC) are widely used for speech recognition. However, MFCC-based speech recognition performance degrades in presence of additive noise. In this paper, we propose a set of noise-robust features based on conventional MFCC feature extraction method. Our proposed method consists of two steps. In the first step, Mel sub-band spectral subtraction is carried out. The second step consists of estimating SNR in each sub-band and defining a weight parameter based on this estimation. The weighting has been carried out in a way that gives more important roles in cepstrum parameter formation to sub-bands that are less affected by noise. Experimental results indicate that this method achieves improved performance for ASR in noisy environments. Furthermore, due to the simplicity of the implementation of our method, its computational overhead relative to MFCC is quite small.

A. Ziaei

Papers

A novel approach for contrast enhancement based on Histogram Equalization

A Novel Approach for Contrast Enhancement in Biomedical Images Based on Histogram Equalization

Weighting of Mel Sub-bands Based on SNR/Entropy for Robust ASR

Spoken Language Identification Using a New Sequence Kernel-based SVM Back-end Classifier

A new MFCC improvement method for robust ASR