Re-blurred Image Research Articles

Confocal microscopy based on dual blur depthmeasurement.

In this paper, we propose a confocal microscopy based on dual blur depth measurement (DBCM). The first blur is defocus blur, and the second blur is artificial convolutional blur. First, the DBCM blurs the defocus image using a known Gaussian kernel and calculates the edge gradient ratio between it and the re-blurred image. Then, the axial measurement of edge positions is based on a calibration measurement curve. Finally, depth information is inferred from the edges using the original image. Experiments show that the DBCM can achieve depth measurement in a single image. In a 10×/0.25 objective, the error measured for a step sample of 4.7397µm is 0.23µm. The relative error rate is 4.8%.

No-Reference Blur Assessment Based on Re-Blurring Using Markov Basis

Blur is produced in a digital image due to low pass filtering, moving objects or defocus of the camera lens during capture. Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result. The high-quality input is relevant to communication service providers and imaging product makers because it may help them improve their processes. Human-based blur assessment is time-consuming, expensive and must adhere to subjective evaluation standards. This paper presents a revolutionary no-reference blur assessment algorithm based on re-blurring blurred images using a special mask developed with a Markov basis and Laplace filter. The final blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images. The objective scores are generated by applying proposed algorithm on the two image databases i.e., Laboratory for image and video engineering (LIVE) database and Tampere image database (TID 2013). Finally, on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient (PLCC), Spearman rank-order correlation coefficient (SROCC), Mean absolute error (MAE), Root mean square error (RMSE) and Outliers ratio (OR). The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur (JNB), Cumulative Probability Distribution of Blur Detection (CPBD) and Edge Model based Blur Metric (EMBM). The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB, CPBD and EMBM algorithms.

Depth Estimation from a Single Image Based on Cauchy Distribution Model

Most approaches to estimate a scene’s 3D depth from a single image often model the point spread function (PSF) as a 2D Gaussian function. However, those methods are suffered from some noises, and difficult to get a high quality of depth recovery. We presented a simple yet effective approach to estimate exactly the amount of spatially varying defocus blur at edges, based on a Cauchy distribution model for the PSF. The raw image was re-blurred twice using two known Cauchy distribution kernels, and the defocus blur amount at edges could be derived from the gradient ratio between the two re-blurred images. By propagating the blur amount at edge locations to the entire image using the matting interpolation, a full depth map was then recovered. Experimental results on several real images demonstrated both feasibility and effectiveness of our method, being a non-Gaussian model for DSF, in providing a better estimation of the defocus map from a single un-calibrated defocused image. These results also showed that our method was robust to image noises, inaccurate edge location and interferences of neighboring edges. It could generate more accurate scene depth maps than the most of existing methods using a Gaussian based DSF model.

Forensics of visual privacy protection in digital images

Visual privacy protection (VPP) is to protect individual’s privacy information in digital images or videos against being seen, such as portrait etc. Once being protected, the privacy may be imperceptible. Forensics of visual privacy protection is becoming a new challenge. As a main visual privacy protection technology, blur operation is always used in VPP. When using the existing approaches such as blur segmentation to detect the images, natural blur or other solid color regions will be falsely alarmed, resulting in low precision. In this paper, we present a novel metric invisibility degree (IvD) to measure the privacy protected blur degree of each pixel. The proposed IvD is defined by calculating the similarity between the test image and the re-blurred image in joint transformation and spatial domain, which could significantly enhance the difference between the privacy protected blur region and the other regions. Then, an effective method based on IvD is developed to automatically forensic and localize the visual privacy protection regions. Firstly, the IvD in block DCT domain of each pixel is calculated, and a IvD map is obtained. Secondly, using the IvD map, the test image is segmented and followed by morphological operation to decrease the mis-alarm regions. Finally, a spatial texture feature descriptor, including gray statistics, smoothness and information capacity, is developed, based on which the detection result is further refined. Experimental results show that the proposed method can detect the privacy regions accurately.

Blind restoration of space-variant Gaussian-like blurred images using regional PSFs

In this paper, we propose an alternative de-blurring method to remove Gaussian blurs (that approximate distortions such as out-of-focus and long atmospheric path). Our research focuses on images that contain space-variant blurs, as well as focused objects within the same image. We propose to use estimated depth map-based segmentation for space-variant blind image de-blurring. Unlike previous studies that assume PSF or use one calculated PSF for the entire image, our method composes re-blurred images using different calculated PSF’s and combining the restored images to one restored image. The proposed method contains several parts: blur map estimation based on edge-width analysis, division of the image into several layers according to the blur severity, and extraction of the point-spread function (PSF) that characterizes the blur at each layer. Next, each layer is de-blurred using its specific PSF and the total variation method; the de-blurred layers are combined forming the final restored image. A main challenge is to prevent distortions of the sharper objects in the image while simultaneously sharpening the blurrier objects.

Fast detection and segmentation of partial image blur based on discrete Walsh–Hadamard transform

Image signals can be blurred due to defocus or motion. Blur may be undesirable for image sensing, but may also contain useful information. Therefore, detecting the blurriness of each pixel and segmenting the partial blur regions in natural images are important and yet challenging in the field of machine vision. A concise no-reference method based on discrete Walsh–Hadamard transform is proposed to detect and segment partial blur in this paper. First, a re-blurring strategy is performed over multiple overlapping image patches extracted from the test image. Then, for both test image and re-blurred image, discrete Walsh–Hadamard transforms are utilized in each image patches to obtain the blur map. This blur map can characterize the blurriness of each pixel in test image. Based on it, combined with K-Means clustering and region-growing, the test image can be segmented into blurry/non-blurry regions. The experiments, performed on a public dataset, demonstrate the capability of the proposed metric in the detection and segmentation of the blur region. Comparative results with the state-of-the-art show the superiority of the proposed approach in image segmentation for both defocus and motion blur images. The proposed approach is compendious without data training and possesses a high time efficiency because of the fast sequency transform.

Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude

Features that exhibit human perception on the effect of blurring on digital images are useful in constructing a blur image quality metric. In this paper, we show some of the exact Zernike moments (EZMs) that closely model the human quality scores for images of varying degrees of blurriness can be used to measure these distortions. A theoretical framework is developed to identify these EZMs. Together with the selected EZMs, the gradient magnitude (GM), which measures the contrast information, is used as a weight in the formulation of the proposed blur metric. The design of the proposed metric consists of two stages. In the first stage, the EZM differences and the GM dissimilarities between the edge points of the test image and the same re-blurred image are extracted. Next, the mean of the weighted EZM features are then pooled to produce a quality score using support vector machine regressor (SVR). We compare the performance of the proposed blur metric with other state-of-the-art full-reference (FR) and no-reference (NR) blur metrics on three benchmark databases. The results using Pearson׳s correlation coefficient (CC) and Spearman׳s ranked-order correlation coefficient (SROCC) for the LIVE image database are 0.9659 and 0.9625 respectively. Similarly, high correlations with the subjective scores are achieved for the other two databases as well.

Non-iterative and spatial domain focus map estimation based on intentional re-blur from a single image (NasBirSi)

Transforming conventional pre-existing 2D content is still a necessary technique for utilizing pre-existing content for stereo vision. In this paper, a spatial domain focus map estimation algorithm based of an intentional re-blur on a single image, which is the only input data, is presented. The proposed intentional re-blur amplifies the blur in the original input image, and the value of our proposed ratio map is the ratio of the gradient magnitude of the intentional re-blurred image to the gradient magnitude of the original image. The edges of the original input image were re-blurred such that the widths of the edges on the ratio map differ. According to the theoretically derived results and the observations of the example used, the widths of the edges on the introduced ratio map were found to be related to the distance of each object from the camera focus. The pixels close to the focal point of the camera are on a thinner edge. In contrast, the edges are wider when the pixels are farther from the focal point. We utilized this discovery to derive the final focus map, whose enhanced effectiveness, quality, and precision were demonstrated experimentally.

Blind image blur assessment using singular value similarity and blur comparisons.

The increasing number of demanding consumer image applications has led to increased interest in no-reference objective image quality assessment (IQA) algorithms. In this paper, we propose a new blind blur index for still images based on singular value similarity. The algorithm consists of three steps. First, a re-blurred image is produced by applying a Gaussian blur to the test image. Second, a singular value decomposition is performed on the test image and re-blurred image. Finally, an image blur index is constructed based on singular value similarity. The experimental results obtained on four simulated databases to demonstrate that the proposed algorithm has high correlation with human judgment when assessing blur or noise distortion of images.

주파수 도메인 정보를 이용한 영상의 Sharpness 평가 방법

본 논문에서는 영상의 선명도를 영상의 주파수 도메인 정보를 이용하여 측정하는 새로운 무기준법 화질 평가 방법을 제안한다. 기존에, 영상에 대한 선명도는 일반적으로 영상의 픽셀 값을 이용하여 측정되었다. 제안된 방법은 기존 방법과 달리 영상에 대한 선명도를 주파수 도메인 정보를 이용하여 측정하였다. 주파수 도메인에서 선명도를 평가하기 위하여 주어진 영상은 가우시안 저주파 필터를 사용하여 열화 되고, 열화 영상과 주어진 영상의 주파수 영역 계수를 사용하여 새로운 선명도 평가 함수를 정의하였다. 제안된 방법의 성능 검증은 TID2008 화질 평가 데이터베이스를 사용하여 이루어졌다. 기존 무기준법 영상 선명도 평가 방법과 비교하였을 때, 제안된 선명도 평가 방법은 주관적 화질 점수와 보다 높은 상관도를 보였다. In this paper, a new no-reference sharpness measure using frequency domain coefficients is proposed. Although most existing sharpness measures used pixel intensity to compute the blur degree, the proposed sharpness measure computes the sharpness using frequency coefficients. To assess the perceived sharpness of a given image, the image is re-blurred by a Gaussian low pass filter and a new quality measure function was defined using the frequency domain coefficients of the given image and the re-blurred image. To evaluate the proposed algorithms, TID2008 quality assessment database was used. Experimental results show that the proposed quality assessment method showed high correlation with the subjective scores.

Defocus map estimation from a single image

In this paper, we address the challenging problem of recovering the defocus map from a single image. We present a simple yet effective approach to estimate the amount of spatially varying defocus blur at edge locations. The input defocused image is re-blurred using a Gaussian kernel and the defocus blur amount can be obtained from the ratio between the gradients of input and re-blurred images. By propagating the blur amount at edge locations to the entire image, a full defocus map can be obtained. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimation of the defocus map.

No-reference blur index using blur comparisons

Presented is a new no-reference blur index for still images that is based on the observation that it can be difficult to perceive between versions of an image blurred to different degrees. A ‘re-blurred’ image is produced by intentionally blurring the test image. Local sample statistics are computed in the vicinity of detected edges of the original and re-blurred images, respectively. These are differenced and normalised to construct a new blur index. Experimental results on four simulated blur databases and on the Real Blur Image Database show that the proposed method obtains high correlations with test subjective quality evaluations.