Robust Deblurring Software Crack Keygen [UPD]

Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers such as saturated pixels and non-Gaussian noise, are present. While some existing non-blind deblurring algorithms can partially deal with outliers, few blind deblurring methods are developed to well estimate the blur kernels from the blurred images with outliers. In this paper, we present an algorithm to address this problem by exploiting reliable edges and removing outliers in the intermediate latent images, thereby estimating blur kernel robustly. We analyze the effects of outliers on kernel estimation and show that most state-of-the-art deblurring methods may recover delta kernels when blurred images contain significant outliers. We propose a robust energy function which describes the properties of outliers for the final latent image restoration. Furthermore, we show that the proposed algorithm can be applied to improve existing methods to deblur images with outliers. Extensive experiments on different kinds of challenging examples demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.

Blind motion deblurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. Although significant progress has been made on tackling this problem, existing methods, when applied to highly diverse natural images, are still far from stable. This paper focuses on the robustness of blind motion deblurring methods toward image diversity-a critical problem that has been previously neglected for years. We classify the existing methods into two schemes and analyze their robustness using an image set consisting of 1.2 million natural images. The first scheme is edge-specific, as it relies on the detection and prediction of large-scale step edges. This scheme is sensitive to the diversity of the image edges in natural images. The second scheme is nonedge-specific and explores various image statistics, such as the prior distributions. This scheme is sensitive to statistical variation over different images. Based on the analysis, we address the robustness by proposing a novel nonedge-specific adaptive scheme (NEAS), which features a new prior that is adaptive to the variety of textures in natural images. By comparing the performance of NEAS against the existing methods on a very large image set, we demonstrate its advance beyond the state-of-the-art.

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Diffusion Probabilistic Models (DPMs) have recently been employed for image deblurring, formulated as an image-conditioned generation process that maps Gaussian noise to the high-quality image, conditioned on the blurry input. Image-conditioned DPMs (icDPMs) have shown more realistic results than regression-based methods when trained on pairwise in-domain data. However, their robustness in restoring images is unclear when presented with out-of-domain images as they do not impose specific degradation models or intermediate constraints. To this end, we introduce a simple yet effective multiscale structure guidance as an implicit bias that informs the icDPM about the coarse structure of the sharp image at the intermediate layers. This guided formulation leads to a significant improvement of the deblurring results, particularly on unseen domain. The guidance is extracted from the latent space of a regression network trained to predict the clean-sharp target at multiple lower resolutions, thus maintaining the most salient sharp structures. With both the blurry input and multiscale guidance, the icDPM model can better understand the blur and recover the clean image. We evaluate a single-dataset trained model on diverse datasets and demonstrate more robust deblurring results with fewer artifacts on unseen data. Our method outperforms existing baselines, achieving state-of-the-art perceptual quality while keeping competitive distortion metrics.

Abstract:Recently, real-time human age estimation based on facial images has been applied in various areas. Underneath this phenomenon lies an awareness that age estimation plays an important role in applying big data to target marketing for age groups, product demand surveys, consumer trend analysis, etc. However, in a real-world environment, various optical and motion blurring effects can occur. Such effects usually cause a problem in fully capturing facial features such as wrinkles, which are essential to age estimation, thereby degrading accuracy. Most of the previous studies on age estimation were conducted for input images almost free from blurring effect. To overcome this limitation, we propose the use of a deep ResNet-152 convolutional neural network for age estimation, which is robust to various optical and motion blurring effects of visible light camera sensors. We performed experiments with various optical and motion blurred images created from the park aging mind laboratory (PAL) and craniofacial longitudinal morphological face database (MORPH) databases, which are publicly available. According to the results, the proposed method exhibited better age estimation performance than the previous methods.Keywords: age estimation; deep ResNet-152; CNN; optical and motion blurring; visible light camera sensor

The interesting thing about SmartDeblur was that it provided an accessible demo that generated and applied a more sophisticated blur kernel than say Photoshop. The actual deblurring was slow and poor, so I took the kernel and used more robust apps to do the actual restoration.

Motivated by the above analysis, we propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior (JDM-BISR). The framework of our JDM-BISR is shown in Figure 1. Inspired by JRM-DSR [18], our JDM-BISR also assumes that if the blurry image can be correctly restored, it can lead to a sparse representation of the dictionary constructed by the reference image. Different from JRM-DSR, we obtain the sparse representation coefficients in blurred invariant space rather than original pixel space, thus improving the accuracy of the sparse representation prior, thereby facilitating the following deblurring and matching tasks. Moreover, since the dimension of the blur invariant is much lower than the original pixel vector, our method can also reduce the computation time of sparse representation and speed matching. We adopt the alternating minimization algorithm to solve the JDM-BISR model. The experimental results demonstrate that our JDM-BISR method performs favorably against the state-of-the-art blurred image matching approaches.

The main contributions of this paper are as follows:(i)We propose a joint image deblurring and matching method with blurred invariant-based sparse representation prior, to deal with the problem of blurred image matching.(ii)We extract pseudo-Zernike moment with blurred invariants from images and obtain the sparse representation coefficients in blurred invariant space, which alleviates the influence of image blurring and improves the reliability of the sparse representation prior.

The remainder of the paper is organized as follows. We will review the related works of pseudo-Zernike moment with blurred invariants and image matching in Section 2. In Section 3, we will detail the model of joint image deblurring and matching method with blurred invariant-based sparse representation prior. Experimental results and analysis will be presented in Section 4. Finally, we will conclude our work in Section 5.

Pseudo-Zernike blurred invariants are based on orthogonal pseudo-Zernike moments and are suitable for blur point spread functions with circular symmetry, and they have blur invariance and noise robustness. The computation of blur invariants of pseudo-Zernike moments needs to compute pseudo-Zernike moments first and then generate different orders of invariants via an iterative way. Specifically, for a polar coordinate image , the pseudo-Zernike moments of order p with repetition q are defined as follows [20]:where . Since is symmetrical to q, we only consider the case where .

Image matching has been intensively studied over the past decade due to its crucial role in computer vision. Traditional image matching methods have been classified into two classes [22]: feature-based methods and pixel-based methods. Feature-based methods first extract feature vectors from the real-time image and the reference image and then measure the similarity among the feature vectors, thereby obtaining the position of the real-time image. Following are some feature-based methods: Canny operator [23], Harris operator [24], SUSAN operator [25], SIFT feature descriptor [26], SURF operator [27], and ridgelet transform [28]. However, these methods perform poor when the input image is blurred, since it is hard to extract robust feature vector from the degenerated images. Since the pixel-based approaches utilize all of the pixels in the local window, they can achieve better performance than the feature-based approach under occlusion conditions. Many pixel-based methods are also proposed, e.g., template matching (TM) [29], increment sign correlation [10], binary coding and phase correlation [30], and selective correlation coefficient [9]. Recently, some cross-correlation-based methods [8, 31, 32] have also been proposed to improve the matching performance. Yoo and Ahn [8] utilized correlation coefficient of occlusion-free matching to determine the position of the real-time image. Bilal and Masud [31] reduced the search speed by applying a monotonically increasing cross-correlation function. Zhu and Deng [32] proposed a gradient direction selection cross-correlation method for image matching. However, the above methods cannot efficiently deal with the problem of blurred image matching.

However, they both obtained the sparse representation coefficients in the original pixel space, which do not adequately consider the influence of image blurring, thus leading to an inaccurate estimation of sparse representation prior. In this paper, we obtain the sparse representation coefficients in blurred invariant space rather than original pixel space, thus improving the accuracy of the sparse representation prior, thereby facilitating the following deblurring and matching tasks. be457b7860