By Sebastiano Battiato
This publication constitutes the completely refereed lawsuits of the sixteenth overseas convention on complex innovations for clever imaginative and prescient structures, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been conscientiously chosen from 129 submissions.
Read or Download Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings PDF
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings
15–22, 2015. 1007/978-3-319-25903-1 2 16 A. Nasonov et al. General purpose blur estimation methods can also be used for sparse defocus map construction problem. The method  is based on the assumption that the blur of the image is close to Gaussian. The image is divided into blocks, and the blur kernel is supposed to be uniform inside the block. The estimation of the blurriness of the block is based on the maximum of diﬀerence ratio between an original image and its two re-blurred versions. Block-based approach provides good blur estimation for highly textured areas but it shows inadequate results for blocks not containing edges, for example, ﬂat areas.
An example of ringing eﬀect is shown in Fig. 1. The source of the ringing eﬀect is high-frequency information corruption or loss . e. image and video compression, deblurring, resampling, denoising, and in analog video . There are many papers that consider the problem of ringing eﬀect detection and suppression in JPEG and JPEG2000 images [7,11]. On the other hand there are only few works that pose the problem of general ringing detection and suppression [13,17,23,24]. Suppression algorithms are based on the connection of ringing eﬀect with total variation , and total variation minimization proposed for image enhancement in .
3. Output: result. 1 as this is an acceptable accuracy for the task. 1 Defocus Blur Estimation Sparse Defocus Map The previous section deals with isolated edge proﬁles with values from 0 to 1. In practice real image edge proﬁles are rarely isolated, and all of them have diﬀerent amplitude. For sparse blur map, we have to obtain edge map using an edge detector (we use Canny edge detector ). For each edge pixel we construct an edge proﬁle along the gradient direction at this pixel using an interpolation method (we use bilinear interpolation).