Digital images are widely used nowadays. However the quality of a digital image would be deteriorated by the corruption of impulse noise in the acquisition or transmission, casusing the errorneous judgement for the human eye or machine. How to effectively remove this impulse noise for a corrupted image is an important research task. Most of the image denoising methods process each noise-corrupted pixel from the top-left to the bottom-right of the images using a sliding window. The regions first processed will affect the subsequent image area. If a heavily noise-corrupted region is firstly reconstructed, restored pixels will deteriorate subsequent processed pixels. This enables the denoised image quality to be reduced. In this thesis, we present a new approach to change the process order of noise corrupted pixels according to the confidence measured with each pixel of an image. An analysis window with a greater quantity of noise-free pixels and with a consistent pixel change direction is defined as a high confidence region and denoised firstly, enabling the quality of the denoised image to be reduced. Experimental results show that the proposed approach can further improve the performance of an image denoising method which utilizes the sliding window from the top-left to the bottom-right. Accordingly, the confidence measure is helpful for image denoising and can be further applied for image signal processing.