A SURVEY ON EFFICIENT NO-REFERENCE BLUR ESTIMATION METHODS

A Survey on Efficient No-reference Blur Estimation Methods

Blur estimation in image processing has come to be of great importance in assessing the quality of images. This work presents a survey of no-reference blur estimation methods. A no-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-refere...

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

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...

A No-Reference Image Blur Metric

This paper presents a no-reference image quality measure, targeted towards blur distortions based on the study of human blur perception for varying contrast values. Utilizing sensitivity of human blur perception at different contrasts, a probabilistic framework is developed. This framework is used to estimate the probability of detecting blur at each edge in an image. The blur perception inform...

A no-reference perceptual blur metric

In this paper, we present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimiza...

No-reference blur assessment based on edge modeling