Page 354 Contrast Enhancement Algorithm for Colour Images Jogu Ramesh M.Tech (DSCE), Scient Institute of Technology, Ibrahimpatnam, Rangareddy Dist, Hyderabad. P.Ramesh Assistant Professor, Scient Institute of Technology, Ibrahimpatnam, Rangareddy Dist, Hyderabad. Abstract: Conventional contrast enhancement techniques often fail to produce satisfactory results for low-contrast images, and cannot be automatically applied to different images because processing parameters must be specified manually to produce satisfactory results for a given image. This paper proposes a contrast enhancement technique to enhance colour images captured under poor illumination and varying environmental conditions. Images are converted from RGB to HSV colour space where enhancement is achieved and reconverted to the RGB. Class Limited Adaptive Histogram Equalization (CLAHE) is used to enhance the luminance component (V). Discrete Wavelet Transform is applied to the Saturation (S) components, and the decomposed approximation coefficients are modified by a mapping function derived from scaling triangle transform. The enhanced S component is obtained through Inverse Wavelet transforms. The image is then converted back to the RGB colour space. Subjective (visual quality inspection) and objective parameters (Peak-signal-to- noise ratio (PSNR), Absolute MeanBrightness Error (AMBE) and Mean squared error (MSE)) were used for performance evaluation. The algorithm implemented in MATLAB was tested images and compared with outputs of HEand CLAHE enhancement techniques. The result shows that the new algorithm gave the best performance of the three methods. Keywords: Contrast Enhancement; Class Limited Adaptive Histogram Equalization (CLAHE) 1. INTRODUCTION: Contrast is the difference in luminance or intensity level between objects or regions in an image. If the contrast is too low, all pixels are a mid-shade of gray making the objects to fade into each other. Hence, low contrast causes loss of information in some areas in the image, while good contrast makes objects or scenes depicted in an image distinguishable and visually interpretable for human and machine analysis. Many algorithms for achieving contrast enhancement have been developed; among them is histogram equalization technique that is attractive due to its simplicity. Histogram equalization generates a grey map that changes the histogram of an image and redistributes all pixel values to be as close as possible to a user- specified desired histogram [1, 2]. An adaptation of histogram equalization is the contrast limited adaptive histogram equalization (CLAHE). CLAHE divides input image into a number of equal size blocks and thenperforms contrast limited histogram equalization on eachblock. The contrast limiting is done by clipping the histogram before histogram equalization [2]. Other colour enhancement methods have been proposed based on histogram equalization [3,4], these also include multi-scale approaches [5-10] and other hue preservation contrast enhancement schemes [11-14]. Earlier works have also shown that the performance of HSV colour space is good in colour improvement [13]. Hue preservation methods keep the Hue constant to avoid the problem of colour shifting, while either only the Luminance (V) component or both Luminance (V) and Saturation (S) components are modified to make the image soft and vivid.