Noise-immune image blur detection via sequency spectrum truncation

Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost–benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average F1 score of 0.887, MAE of 0.101, detecting time of 0.7 s, and training time of 0.5 s. Especially for noise-polluted blurry images, the proposed method achieves the F1 score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost–benefit advantage and noise immunity that has great application prospect in complex sensing environment.

Fast Defocus Blur Detection Network via Global Search and Local Refinements

Defocus blur detection aims at separating regions on focus from out-of-focus for image processing. With today’s popularity of mobile phones with portrait mode, accurate defocus blur detection has received more and more attention. There are many challenges that we currently confront, such as blur boundaries of defocus regions, interference of messy backgrounds and identification of large flat regions. To address these issues, in this paper, we propose a new deep neural network with both global and local pathways for defocus blur detection. In global pathway, we locate the objects on focus by semantical search. In local pathway, we refine the predicted blur regions via multi-scale supervisions. In addition, the refined results in local pathway are fused with searching results in global pathway by a simple concatenation operation. The structure of our new network is developed in a feasible way and its function appears to be quite effective and efficient, which is suitable for the deployment on mobile devices. It takes about 0.2[Formula: see text]s per image on a regular personal laptop. Experiments on both CUHK dataset and our newly proposed Defocus400 dataset show that our model outperforms existing state-of-the-art methods.

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.

Blur Detection Sensitivity Increases in Children Using Orthokeratology

PurposeTo investigate changes in blur detection sensitivity in children using orthokeratology (Ortho-K) and explore the relationships between blur detection thresholds (BDTs) and aberrations and accommodative function.MethodsThirty-two children aged 8–14 years old who underwent Ortho-K treatment participated in and completed this study. Their BDTs, aberrations, and accommodative responses (ARs) were measured before and after a month of Ortho-K treatment. A two forced-choice double-staircase procedure with varying extents of blur in three images (Tumbling Es, Lena, and Street View) was used to measure the BDTs. The participants were required to judge whether the images looked blurry. The BDT of each of the images (BDT_Es, BDT_Lena, and BDT_Street) was the average value of the last three reversals. The accommodative lag was quantified by the difference between the AR and the accommodative demand (AD). Changes in the BDTs, aberrations, and accommodative lags and their relationships were analyzed.ResultsAfter a month of wearing Ortho-K lenses, the children’s BDT_Es and BDT_Lena values decreased, the aberrations increased significantly (for all, P ≤0.050), and the accommodative lag decreased to a certain extent [T(31) = 2.029, P = 0.051]. Before Ortho-K treatment, higher-order aberrations (HOAs) were related to BDT_Lena (r = 0.463, P = 0.008) and the accommodative lag was related to BDT_Es (r = −0.356, P = −0.046). After one month, no significant correlations were found between the BDTs and aberrations or accommodative lags, as well as between the variations of them (for all, P ≥ 0.069).ConclusionOrtho-K treatment increased the children’s level of blur detection sensitivity, which may have contributed to their good visual acuity.