An Implementation of Image Enhancement on Real Time

An Implementation of Image Enhancement on Real Time Configurable System using HDL: A REVIEW Mahavir Singh, Gitanjali PandoveDepartment of Electronics and CommunicationDeenbandhu Chhotu Ram University of Science & TechnologyMurthal, Sonipat, [email protected][email protected] Abstract – Whenever the image is converted from one form to another, the degradation occur in the quality of the image. To improve the visual quality of image, the image enhancements techniques are used. These Techniques are the contrast stretching, brightness control, invert operation, threshold operation etc. The image enhancement can be done with the help of the both software implementation and hardware implementation. But as the hardware implementation has better performance than the software implementation therefore we use the real time configurable system to enhance the image quality. Image processing using hardware description language is the new approach in the field of digital design using VLSI.Keywords – FPGA, Digital Image Processing, Verilog, Hardware Description Language, Image EnhancementI. INTRODUTIONImage processing is the technique that is used to convert the image in to the digital form and then perform some operation on it, to enhance the image quality. Mainly there are three major problems due to which image processing is developed, these are image digitization, image enhancement and restoration and image segmentation. A digital image is an image that has been represent using a discrete quantity both in the spatial coordinates and in  the brightness. A digital image is formed of the pixels. This pixel is illuminated by the light. Grey level is the digitized brightness value and it is represented by 2m. The number of bits B, require to store the image of size n*n is given by                                       B=n*n*m bits Digital image processing (DIP) is the process to manipulate and analyze image with computer. Digital image processing is used in various fields of modern society and especially in medical imaging and recently occupies a fundamental role in assisted diagnoses of diseases, developing a multidisciplinary field 2. Image enhancement can be done in the two domains. One is the spatial domain and other one is the frequency domain.Fig.1 Domains for the implementation of the image enhancementThe spatial domain is based on the direct manipulation of pixels and the frequency domain modifies the Fourier transform of an image. The image enhancement in spatial domain the following processing methods are used like inverting image, contrast manipulation, threshold operation, brightness manipulation. To implement the applications that involve processes like image enhancement could be easier on general purpose computer but it is not the time efficient because of additional constraints and other peripheral devices. Hardware implementation offers greater speed than the software implementation. Implementation of complex computational tasks on hardware by using parallelism and pipelining, reduce the execution time. Implement the image processing algorithms on FPGA minimize the time-to-market cast and simplifies the debugging and the verification. The features of FPGAs are larger I/O bandwidth to memory, parallelism, pipelining and optimizing compiler. The features make FPGAs superior in speed as compared to other hardware. Therefore, for implementation of image processing, FPGA is an ideal choice because it boosts the performance of the imaging algorithms like image construction, image analysis, enhancement and restoration, image and data compression etc. It is assumed that mainly impulse noise or Gaussian noise is present in the image. Impulse noise is also known as the spec noise or shot noise. This type of noise changes the value of the some pixels of the image. This means some white pixels become black and some white pixels become black. That’s why this type of noise also known as salt and pepper noise. The impulse noise only affects the some pixels but the Gaussian noise affects the values of all pixels. The image can be enhanced when we   remove additive noise, multiplicative interference, increase its contrast and decrease its blurring.   II. LITERATURE REVIEWLuliana et al. 1: have discussed new series of filters developed at the hardware level for image processing (edges detection, sharpen operation, enhance contrast operation and brightness-adjustment), in order to improve the quality of images and to assist in diagnosis the medical specialists. Luliana et al. 3: have discussed the image enhancement methods using the hardware description language (Verilog). In this image is enhanced by using the point operation on image processing like brightness enhancement, contrast stretching ,inverting and thresholding operations.Grigore T. Popa et al. 7: have discussed the advantages of using FPGA over softwares and DSPs as a platform for implementation of digital image processing applications. This work presents a hardware implementation of a FPGA-based real-time configurable system consisting of image processing low-level operators such as, contrast adjustment, brightness adjustment, inverting an image and pseudo-color operation.  N Sachdeva et al. 16: have discussed the efficient implementation of mean formula into hardware base component using FPGA device.Yahia said  et al. 12: have discussed to improve the implementation time, Xilinx AccelDSP, a software for generating hardware description language (HDL) from a high-level MATLAB description has been used.       Kalyani A. Dakre et al. 11:  have discussed the focus on implementation issues of image enhancement algorithms like brightness control, contrast stretching, negative transformation, thresholding, filtering techniques on FPGA that have become a competitive alternative for high performance digital signal processing applications.III. METHODS OF IMAGE ENHANCEMENTThese methods are used to increase the visual quality of the images. The results of images processed are more suitable for the specific application than the original image. Fig. 2 Methods to enhance the imageNoise removal and contrast stretching are the operations that are usually used for the image enhancement. In this section discusses the commonly used methods of image enhancement like,1) Brightness control2) Histogram Equalization3) Contrast StretchingA. Brightness ControlWith the help of energies recorded by sensors, the brightness for different pixels is created. Controlling brightness is the technique to increase the grey levels of each pixel such that it enhance the image quality with lower brightness. If the digital images are captured in poor brightness then the visuality of the image will not be clear. To resolve this problem, we can increase the brightness of the image and this make the image clearer. In low brightness image, in histogram most of the pixel lies in left half of gray value range. We can increase the brightness of the image by adding constant to the gray value of every pixel of image. This shifts the histogram towards the brighter side with the constant value. But here we should the constant value wisely so that the grey value ranges within 0-255.  If the grey level value increased above the 255, then it looses the information that the image carry. Brightness control algorithm is given by:                    IK(r,c)+h ,   if IK(r,c) + h<=255Jk(r,c)=       255          ,   if Ik(r,c)  +  h>255h>=0 and k belongs to 1, 2, 3 that is the band index. IK(r,c) is the color level of input pixels and  Jk(r,c) is of output pixels(r,c). Therefore to increase the brightness of the image, the grey level must be lies from 0 to 255. B. Histogram equalizationThe histogram of an image is that diagram which shows frequency of every intensity value from pixel element of image. The high value of the histogram shows that with this intensity the numbers of pixels is high. If the histogram value is low, then the number of pixels at that intensity is low. Brightness and contrast of the image can be shown by the histogram.Consider an image with  p×q pixels. The histogram can be calculated mathematically as                          H(i) =  ?_(p=0)^p??_(q=0)^q?kWhere k=1 if f(p,q) = i and k=0 if f(p,q) ? i. and f(p,q) is the value of intensity at the co-ordinate (p,q) in image. The method of histogram equalization transforms the value of intensity such that output image histogram matches with uniform histogram. Generally this method increases the global contrast of the image. It allows lower contrast to acquire a higher contrast. This type of method is used for when both the foregrounds and backgrounds are bright and dark.The formula for histogram equalization formula is given by: