Abstract
Present day applications require various kinds of images and pictures as sources of information for interpretation and analysis. Whenever an image is converted from one form to another, such as digitizing, scanning, transmitting, storing, etc., some form of degradation occurs at the output. Hence, the output image has to undergo a process caUed image enhancement which consists of a coUection of techniques that seek to improve the visual appearance of an image. This work addresses the implementation of image enhancement algorithms like brightness control, contrast adjustment and histogram equalization on FPGA that have become a competitive alternative for high-performance digital signal processing applications.
I. INTRODUCTION
Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Meanwhile, the term image enhancement is mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Enhancement of images can be carried out in intensity domain, spatial domain or in frequency domain. Some of the intensity domain methods are brightness control, contrast enhancement, histogram equalization, gamma correction and histogram modification. These are termed as point processing operations on image. Spatial domain methods include noise reduction, edge enhancement and sharpening filters. These are mask processing operations since they take into account neighboring pixel intensities also in the original image for computing the intensity value in the resulting image. Frequency domain methods are low pass filtering and high pass filtering. One of the example as to why image enhancement is necessary is that an image might be taken of an endothelial cell, which might be of low contrast and somewhat blurred. Reducing the noise and blurring and increasing the contrast range could enhance the image. Contrast stretching is commonly used for enhancing the quality and contrast of medical images. Histogram equalization has been widely used in diverse fields that include radiology, thermal imaging, radar image processing Roy Paily Department of Electronics and Communication Engineering Indian Institute of Technology Guwahati Guwahati-781039, Assam, India Email: roypaily[at]iitg.ernet.in and speech recognition. Image enhancement is also important for forms processing or Optical Character Recognition applications in order to improve character recognition. Implementing image processing algorithms on reconfigurable hardware minimizes the time-to-market cost, enables rapid prototyping of complex algorithms and simplifies debugging and verification. Some of the image processing algorithms that has been implemented on FPGA in literature is discussed. The description of existing works are grouped as follows: edge detection, hough transform, image compression, segmentation, DCT and DWT computation, watermarking and image enhancement algorithms. H. Neoh and A. Hazanchuk have presented implementation of the Canny edge detection algorithm and Prewitt filter [1]. S. Hirai et. al [2] have implemented three image processing algorithms for computation of the image gravity center, detection of object orientation using a radial projection and computation of Hough transform. In [3], a realtime architecture for Hough Transform based on CORDIC algorithm and gradient for straight lines detection is described. Richard Bannister et. al have implemented image segmentation algorithm using logarithmic arithmetic on FPGA [5]. Hardware implementation of 8x8 DCT and IDCT on different FPGA technologies using the modified Loeffler algorithm is presented in [6]. FPGA implementation of Discrete Wavelet Transform(DWT) and modified Set Partitioning in Hierarchial Trees(SPIHT) algorithm for lossless image compression is proposed in [7]. FPGA implementation of image adaptive watermarking using Human Visual Model is presented in [8]. The algorithm was developed using the human visual system based on Discrete Hadamard Transform.


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