A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

Evaluation of a Motion Correction Algorithm for C-Arm Computed Tomography Acquired During Transarterial Chemoembolization

Purpose The aim of this retrospective study was to evaluate the feasibility of a motion correction 3D reconstruction prototype technique for C-arm computed tomography (CACT). Material and Methods We included 65 consecutive CACTs acquired during transarterial chemoembolization of 54 patients (47 m,7f; 67 ± 11.3 years). All original raw datasets (CACTOrg) underwent reconstruction with and without volume punching of high-contrast objects using a 3D image reconstruction software to compensate for motion (CACTMC_bone;CACTMC_no bone). Subsequently, the effect on image quality (IQ) was evaluated using objective (image sharpness metric) and subjective criteria. Subjective criteria were defined by vessel geometry, overall IQ, delineation of tumor feeders, the presence of foreign material-induced artifacts and need for additional imaging, assessed by two independent readers on a 3-(vessel geometry and overall IQ) or 2-point scale, respectively. Friedman rank-sum test and post hoc analysis in form of pairwise Wilcoxon signed-rank test were computed and inter-observer agreement analyzed using kappa test. Results Objective IQ as defined by an image sharpness metric, increased from 273.5 ± 28 (CACTOrg) to 328.5 ± 55.1 (CACTMC_bone) and 331 ± 57.8 (CACTMC_no bone; all p < 0.0001). These results could largely be confirmed by the subjective analysis, which demonstrated predominantly good and moderate inter-observer agreement, with best agreement for CACTMC_no bone in all categories (e.g., vessel geometry: CACTOrg: κ = 0.51, CACTMC_bone: κ = 0.42, CACTMC_no bone: κ = 0.69). Conclusion The application of a motion correction algorithm was feasible for all data sets and led to an increase in both objective and subjective IQ parameters. Level of Evidence 3

Segementation of Blur Images Using Local Binary Pattern Technique

Defocus blur is to a great degree regular in images caught utilizing optical imaging frameworks. It might be bothersome, however may likewise be a deliberate imaginative impact, in this manner it can either upgrade or hinder our visual view of the image scene. For assignments, for example, image restoration and object recognition, one should need to portion an in part blurred image into blurred and non-blurred areas. In this paper, we propose sharpness metric in light of local binary patterns and a hearty segmentation calculation to isolate all through focus image districts. The proposed sharpness metric adventures the perception that most local image fixes in blurry areas have altogether less of certain local binary patterns contrasted and those in sharp districts. Utilizing this metric together with image tangling and multiscale surmising, we got excellent sharpness maps. Tests on several halfway blurred images were utilized to assess our blur segmentation calculation and six comparator techniques. The outcomes demonstrate that our calculation accomplishes similar segmentation comes about with the best in class and have enormous speed advantage over the others. in Extension we are using LLBP (Line Local Binary Pattern ) for getting better output in blur images.