A specialized form of image mosaicing known as image stitching has become increasingly common, especially in the making of panoramic images. The stitching quality is measured visually by the similarity of the stitched image to each of the input images and by the visibility of the seam between the stitched images. In order to define and get the best possible stitching, first of all noise is removed from the input images which is done as a preprocessing step and also there are several formal cost functions for the evaluation of the stitching quality. In these cost functions the similarity to the input images and the visibility of the seam are defined in the gradient domain, minimizing the disturbing edges along the seam. A good image stitching will overcome both photometric inconsistencies and geometric misalignments between the stitched images. This approach is demonstrated in various applications including generation of panoramic images, object insertion and stitching of object parts.