  When an image is formed, factors such as lighting (spectra, source, and
intensity) and camera characteristics (sensor response, lenses) affect the
appearance of the image. Therefore, the prime factor that reduces the quality
of the image is noise. It hides the important details and information of
images. In order to enhance the qualities of the image, the removal of noises
become imperative and that should not at the cost of any loss of image
information. Noise removal is one of the pre-processing stages of image
processing. In this paper a new method for the enhancement of grayscale images
is introduced, when images are corrupted by fixed valued impulse noise (salt
and pepper noise). The proposed methodology ensures a better output for the low
and medium density of fixed value impulse noise as compared to the other famous
filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF)
and Modified Decision Based Median Filter (MDBMF) etc. The main objective of
the proposed method was to improve peak signal to noise ratio (PSNR), visual
perception and reduction in the blurring of the image. The proposed algorithm
replaced the noisy pixel by trimmed mean value. When previous pixel values, 0s,
and 255s are present in the particular window and all the pixel values are 0s
and 255s then the remaining noisy pixels are replaced by mean value. The
gray-scale image of mandrill and Lena were tested via the proposed method. The
experimental result shows better peak signal to noise ratio (PSNR), mean square
error values with better visual and human perception.
