Chromosome images are susceptible to sensor and staining noises, inhomogeneity, and blurring which prevent efficient karyotyping. In this research work, image processing methods are systematically extended for the preprocessing of chromosome images, and a novel approach for denoising and enhancing the chromosome images is proposed. The proposed approach is mathematically modeled and evaluated with subjective and objective measures. Promising results are obtained which are further substantiated with the post-classification of the segmented chromosomes from the preprocessed input image. Performance of the proposed method is quantified in terms of MSE (Mean Squared Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index Measure), FSIM(Features Similarity Index Measure), SAM(Spectral Angle Mapper), and SRE(Signal to Reconstruction Error ratio). An MSE of 8.164, PSNR of 39.037, SSIM of 0.9654, SAM of 81.729, SRE of 63.842, and FSIM of 0.6128 are obtained, on average for a set of 10 test images which were previously degraded with Gaussian noise and Gaussian blur. Post-classification accuracy improved from 88% to 95% as and when the proposed preprocessing is followed by the classification task.