MULTISCALE HAAR TRANSFORM FOR BLUR ESTIMATION FROM A SET OF IMAGES

Abstract. This paper proposes a method to estimate the local sharpness of an optical system through the wavelet-based analysis of a large set of images it acquired. Assuming a space-invariant distribution of image features, such as in the aerial photography context, the proposed approach produces a sharpness map of the imaging device over 16 × 16 pixels blocks that enables, for instance, the detection of optical defects and the qualification of the mosaicking of multiple sensor images into a larger composite image. The proposed analysis is based on accumulating of the edge maps corresponding to the first levels of the Haar Transform of each image of the dataset, following the intuition that statistically, each pixel will see the same image structures. We propose a calibration method to transform these accumulated edge maps into a sharpness map by approximating the local PSF (Point Spread Function) with a Gaussian blur.