GLOBAL CONTRAST ENHANCEMENT USING SMI & PR ALGORITHMS

Image enhancement is one of the challenging issues in low level image processing. In general, it is difficult to design a visual artifact free contrast enhancement method. Considering this, we propose a global, computationally efficient spatial contrast enhancement method which performs enhancement by considering the spatial locations of gray-levels of an image instead of direct use of gray-levels or their co-occurrences. Contrast enhancement is the important factor in image enhancement. Contrast enhancement is used to increase the contrast of an image with low dynamic range and bring out the image details that would be hidden. The enhanced image is looks qualitatively better than the original image if the gray-level differences. This work proposes a novel algorithm, which enhances the low contrast input image by using the spatial information of pixels. This algorithm introduces new method to compute spatial entropy of pixels using spatial distribution of gray levels. This is different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray level distribution or joint statistics computed from gray levels of an image. For each gray level the corresponding spatial distribution is computed by considering spatial location of all pixels having the same gray level in histogram. From the spatial distribution of gray levels of an image entropy can be measured and create distribution which can be further mapped to uniform distribution function to achieve final contrast enhancement. This method achieves contrast enhancement of low contrast image without altering the image if the image’s contrast is high enough. This algorithm considers transform domain coefficient weighting to achieve global and local contrast enhancement of the image. Experimental results show that proposed algorithm produces better enhanced images than existing algorithms.