Digital image processing can often improve the quality of visual sensing of images and real world’s scenes however the optimization of the used algorithms is not always an easy task. The suitable parameter settings of the methods often depend on the features of the scenes and may also depend on the aim of the (further) processing. In this paper, a fuzzy information measure is introduced which evaluates the level of information of pictures. The idea behind the technique is that the amount of information in an image is strongly related to the number and complexity of the objects in the image. The primary information about the objects is usually related to their boundaries, i.e., the characteristic, corner and edge, pixels carry the most relevant information about the image content. The measure presented in this paper sums up the fuzzy level of details and this amount is used to scale the qualification and transformation of images. The presented technique can advantageously be built into different image processing algorithms favorably used in Image Quality Improvement and High Dynamic Range Imaging. The qualification measure may also be applied for increasing the color differentiation capability of the human eye and thus, for improving visual sensing.