Local scale control for edge detection and blur estimation

Abstract

Abstract-The standard approach to edge detection is based on a model of edges as large step changes in intensity. This approach fails to reliably detect and localize edges in natural images where blur scale and contrast can vary over a broad range. The main problem is that the appropriate spatial scale for local estimation depends upon the local structure of the edge, and thus varies unpredictably over the image. Here we show that knowledge of sensor properties and operator norms can be exploited to define a unique, locally computable minimum reliable scale for local estimation at each point in the image. This method for local scale control is applied to the problem of detecting and localizing edges in images with shallow depth of field and shadows. We show that edges spanning a broad range of blur scales and contrasts can be recovered accurately by a single system with no input parameters other than the second moment of the sensor noise. A natural dividend of this approach is a measure of the thickness of contours which can be used to estimate focal and penumbral blur. Local scale control is shown to be important for the estimation of blur in complex images, where the potential for interference between nearby edges of very different blur scale requires that estimates be made at the minimum reliable scale.

Statistical Modeling of Photographic Images

“…Much of the structure in the variance field may be attributed to discontinuous features such as edges, lines, or corners. There is a substantial literature in computer vision describing such structures [e.g., 57,32,17,27,56], but it has proven difficult to establish models that are both explicit and flexible. Finally, there have been several recent studies investigating geometric regularities that arise from the continuity of contours and boundaries [45,16,19,21,60].…”

Adaptive Filtering Using Channel Representations

“…Despite the fact that this implies a rapidly growing loss of information with increasing scale and a singular zero scale, this procedure is insensible from a statistical perspective and does not comply with the notion of scale selection [39,9]. Since the latter argument is not straightforward, we explain our rationale in some more detail.…”

Perceptual blur and ringing metrics: application to JPEG2000

“…While measuring the perceptual blur in an image or a video sequence has not yet been investigated, related research topics include blur identification [6], blur estimation [4,2], image deblurring [1] and blind deconvolution [5]. In practice most of these methods require iterative solving algorithms, which are computationally demanding.…”