Improvements of still image quality by using independent component analysis

in this note, we report several improvements of preceding papers [5], [6] by Koike and Nagasaka, and [10] by Suzuki on the improvements of still image quality by using independent component analysis. Embedding several types of noises to the original image, we have an observed image, to which we apply filtering process and it becomes the second observed image. Then we apply Principal Component Analysis abbreviated to PCA to two above observed images to be uncorrelated. Then each uncorrelated image is transformed to obey normal law by minimizing the fourth cumulant, then transformed images are close to be independent, and this method with PCA is called FastICA. Then we succeeded in obtaining a still image of improved image quality, measured by objective Sound to Noise Ratio abbreviated to SNR. The smoothing filter and a median filter of small size give fairly good improvements of image quality. As for noises, Gaussian noise gives better improvements.