The blur effect: perception and estimation with a new no-reference perceptual blur metric

Abstract

To achieve the best image quality, noise and artifacts are generally removed at the cost of a loss of details generating the blur effect. To control and quantify the emergence of the blur effect, blur metrics have already been proposed in the literature. By associating the blur effect with the edge spreading, these metrics are sensitive not only to the threshold choice to classify the edge, but also to the presence of noise which can mislead the edge detection. Based on the observation that we have difficulties to perceive differences between a blurred image and the same reblurred image, we propose a new approach which is not based on transient characteristics but on the discrimination between different levels of blur perceptible on the same picture. Using subjective tests and psychophysics functions, we validate our blur perception theory for a set of pictures which are naturally unsharp or more or less blurred through one or two-dimensional low-pass filters. Those tests show the robustness and the ability of the metric to evaluate not only the blur introduced by a restoration processing but also focal blur or motion blur. Requiring no reference and a low cost implementation, this new perceptual blur metric is applicable in a large domain from a simple metric to a means to fine-tune artifacts corrections.

Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)

“…It also allows images with excessive blur or tilt to be excluded. Blurry images were detected and removed via a blur metric as developed by Crete et al [25], and a further description of its implementation can be found in Turner et al [26]. Using the positional information for each image, it was also possible to remove the images captured during the UAVs ascent and descent.…”

Wavelet Based Intentional Blurring Variance Scheme for Blur Detection in Barcode Images

“…As the name states, this algorithm is based on intentional blurring of the given image. The principle stated in [8] is straightforward with the observation that, the intentional blurring of a sharp image gives enormous gray scale variations. On the other hand, intentional blurring of an already blurred image gives small gray scale variations.…”

“…This work identifies type of blur and estimates blur parameters using neural network for four categories of blur namely defocus, rectangular, motion and Gaussian. Crete et al [8] used intentional blurring pixel difference (IBD) algorithm due to the fact that it doesn't require the use of edge detection. Another factor in considering IBD algorithm is its computational speed.…”

Single-view recaptured image detection based on physics-based features

“…In this case, the in-focus region is more likely to be of a regular shape, e.g., a rectangular focus region for a printing paper, in contrast to a less regular shape of a 3D object, e.g., silhouette of a human face. The spatial distribution of a blur measure proposed by Crete et al [13] extracted from each image block will form a clue for detecting the above-mentioned scenarios. The dimension of blur feature within each block is 1.…”