Histogram Equalization

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Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.

This work describes a hardware implementation of the histogram equalization algorithm frequently used in image processing for contrast enhancement. The design is intended to process a series of small images (640×480), each with a dynamic range of 8 bits per pixel. Three dual ported 1 MB SRAMs with timing interface of 2 ns (equal input and output delays) are used for storing image being processed (input), resulting equalized image (output) and intermediate data (scratch pad). The RTL design is simulated in Modelsim and synthesized using Synopsys. The performance metric used for design evaluation is the inverse of the product of delay (clock period times number of cycles to process all the images) and cell area of the chip. The goal of design optimization is to maximize the number of images processed per unit cell area. Processing 16 pixels per clock period, a performance metric of 1.5665x10-11 (ns-1.μm-2) is achieved for a clock period of 11.5 ns and cell area of 139978.7766 μm2.

Dentists used to diagnose teeth periapical lesion according to patient’s dental x-ray. But most of the time there were a problematic issue to reach a definitive diagnosis. It takes too much time, case and chief complaint history needed, many tests and tools are needed and sometimes taking too many radiographs is required. Even though, sometimes reaching definitive diagnosis before starting the treatment is difficult. Therefore, the objective of this research is to predict whether the patient has teeth periapical lesion or not and its type using machine learning techniques. The proposed system consists of four main steps: Data collection, image preprocessing using median and average filters for removing noise and Histogram equalization for image enhancement, feature extraction using segmentation and expectation maximization algorithm, and finally machine learning (classification) using Feed Forward Neural Networks and K-Nearest Neighbor Classifier. It has been concluded from the results that the K-Nearest Neighbor Classifier performs better than Feed Forward Neural Network on our real database.

Nowadays X-ray imaging is a commonly used procedure in medical diagnosis. For doctors it is very important to be able to find as much details as possible from the image. Until images were acquired in a digital form it was impossible to alter their appearance. Recently more and more X-ray systems output digital images which can be enhanced using various image processing techniques. One of them is histogram equalization which is known for quite some time. In certain cases its derivation, the so called local histogram equalization, can result in an even better performance. In this paper a research of local histogram equalization is presented for the case of an X-ray image of a bone fracture with a fixture. This image is characterized with wide histogram with very small local variations. Common local histogram equalization technique was further modified in order to enable to see the bone structure even in the areas obscured by the fixture and screws.

A novel color image histogram equalization approach is proposed that exploits the correlation between color components and it is enhanced by a multi-level smoothing technique borrowed from statistical language engineering. Multi-level smoothing aims at dealing efficiently with the problem of unseen color values, either considered independently or in combination with others. It is applied here to the HSI color space for the probability of intensity and the probability of saturation given the intensity, while the hue is left unchanged. Moreover, the proposed approach is extended by an empirical technique, which is based on a hue preserving non-linear transformation, in order to eliminate the gamut problem. This is the second method proposed in the paper. The equalized images by the two methods are compared to those produced by other well-known methods. The better quality of the images equalized by the proposed methods is judged in terms of their visual appeal and objective figures of merit, such as the entropy and the Kullback–Leibler divergence estimates between the resulting color histogram and the multivariate uniform probability density function.

Histogram Equalization (HE) has been an essential addition to the Image Enhancement world. Enhancement techniques like Classical Histogram Equalization(CHE),Adaptive Histogram Equalization (AHE), Bi-Histogram Equalization (BHE) and Recursive Mean Separate Histogram Equalization (RMSHE) methods enhance contrast, brightness is not well preserved, which gives an unpleasant look to the final image obtained. Thus, we introduce a novel technique Multi-Decomposition Histogram Equalization (MDHE) to eliminate the drawbacks of the earlier methods. In MDHE, we have decomposed the input image using a unique logic, applied CHE in each of the sub-images and then finally interpolated them in correct order. The final image after MDHE gives us the best results based on contrast enhancement and brightness preservation aspect compared to all other techniques mentioned above. We have calculated the various parameters like PSNR, SNR, RMSE, MSE, etc. for every technique. Our results are well supported by bar graphs, histograms and the parameter calculations at the end.

In this paper the study of out-of-plane or transverse vibrations in a square plate using digital speckle pattern interferometry (DSPI) is presented. To improve the measurement accuracy, we have implemented a new filtering scheme based on combination of average/median filtering and Symlet wavelet filtering which enhances the signal to noise ratio in the speckle interferogram obtained from DSPI. A large number of fringe patterns are shown for square plate with two different boundary conditions. Experimentally obtained resonance frequencies for the square plate for the boundary condition one edge fixed and other edges being free, the resonance frequencies obtained from DSPI show good agreement with that obtained from classical theory for thin plates.

Write a MATLAB function/script, which performs histogram equalization, and use it to correct the contrast issues with the following images (lightPollen.jpg, lowContrastPollen.jpg, darkPollen.jpg). Compare the histograms of the corrected images to those of the originals. Hints: 1/ Read the image with imread(); 2/ Convert it to double 3/ define L = 256 4/ Count the number of gray-levels in the image in a vector p 5/ normalize p with the number of pixels in the image 6/ calculate transfer function, round its values to nearest integer 7/ apply the transfer function to original pixel values 8/ Plot the histograms (plot) and equalized images (imshow) Exercise: Match histogram of pollen.jpg and lightPollen.jpg

Random numbers are used in a wide variety of applications from simulation and encryption to gambling and clinical trials. A good quality random number generator is an asset for applications like encryption, randomized designs and network and information security. Various mathematical models have been developed in the past to improve the quality of random numbers. It can be construed that in general to obtain random numbers of excellent quality, a complex mathematical model has to be used which can be a performance bottleneck. In this work, we propose a novel technique to implement a True Random Number Generator (TRNG) using sources of uncertainty found within Graphics Processing Units (GPUs) together with histogram equalization to obtain maximum entropy. We evaluate the random numbers generated by our approach using four tests. First, we measure the correlation values between two sequences of random numbers, second, we measure the entropy values, third, we use watermarking, an application used in network security and finally we use Monte Carlo analysis for pi-value calculation. Based on these quality measurements, our method has achieved better results than popular random number generators compared in this work. Furthermore, this approach is a massively scalable solution ideal for high performance computing implementations.

Image enhancement is to process an image so the result will be more suitable than the original image. While in image processing there is collection of techniques which is used to improve the visual form of an image. The main goal of the image enhancement method is to enhance the look and feature of the image. The improve image gave us a clear image to the eye. And also Image enhancement techniques are application dependent. In this paper we used image enhancement techniques by using histogram equalization techniques in Matlab.

Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.

Histogram Equalization is a contrast enhancement technique in the image processing which uses the histogram of image. However histogram equalization is not the best method for contrast enhancement because the mean brightness of the output image is significantly different from the input image. There are several extensions of histogram equalization has been proposed to overcome the brightness preservation challenge. Contrast enhancement using brightness preserving bi-histogram equalization (BBHE) and Dualistic sub image histogram equalization (DSIHE) which divides the image histogram into two parts based on the input mean and median respectively then equalizes each sub histogram independently. This paper provides review of different popular histogram equalization techniques and experimental study based on the absolute mean brightness error (AMBE), peak signal to noise ratio (PSNR), Structure similarity index (SSI) and Entropy

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MLA

Archana Kaushik, Alka Choudhary, “A Novel Algorithm for Dehazing an Image”, December 15 Volume 3 Issue 12 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 6802 - 6807
APA

Archana Kaushik, Alka Choudhary, December 15 Volume 3 Issue 12, “A Novel Algorithm for Dehazing an Image”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 6802 - 6807

The multilayer secured DWT-DCT and YIQ color space based image watermarking technique with robustness and better correlation is presented here. The security levels are increased by using multiple pn sequences, Arnold scrambling, DWT domain, DCT domain and color space conversions. Peak signal to noise ratio and Normalized correlations are used as measurement metrics. The 512x512 sized color images with different histograms are used for testing and watermark of size 64x64 is embedded in HL region of DWT and 4x4 DCT is used. 'Haar' wavelet is used for decomposition and direct flexing factor is used. We got PSNR value is 63.9988 for flexing factor k=1 for Lena image and the maximum NC 0.9781 for flexing factor k=4 in Q color space. The comparative performance in Y, I and Q color space is presented. The technique is robust for different attacks like scaling, compression, rotation etc.

Diabetes is one of the most rapidly increasing health threats worldwide. One of the further abnormalities is on retina (diabetic retinopathy). Early treatment can be conduct from detection of microaneurysms. The main concentration of this paper is the algorithm to detect microaneurysm with mathematical morphology. The mathematical morphology is choosen because microaneurysms tend to have typical shape. Generally, the algorithm is consist of three stages. The first is preprocessing, the second is detecting candidate microaneurysms, and the third is postprocessing handling the process of removing unused features. The performances is evaluated using the database from DIARETDB1 which provides ground truth collected from several experts and a strict evaluation protocol. The optimal performance will be satified when considering green channel obtaining, PAL size image processing, adaptive histogram equalization threshold at 0.03, canny edge detection threshold at 0.16, MAs and optimum microaneurysms size at 5 to 16 pixels.

This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.