Denoising is a fundamental imaging problem. Versatile but fast filtering has been demanded for mobile camera systems. We present an approach to multiscale filtering which allows real-time applications on low-powered devices. The key idea is to learn a set of kernels that upscales, filters, and blends patches of different scales guided by local structure analysis. This approach is trainable so that learned filters are capable of treating diverse noise patterns and artifacts. Experimental results show that the presented approach produces comparable results to state-of-the-art algorithms while processing time is orders of magnitude faster.
The influential non-local means (NLM) filtering @cite_22 has received great interest since its introduction. NLM generalizes bilateral filtering by using patch-wise photometric distance to better characterize self-similarity, but at increased computational cost. Many techniques have been proposed to accelerate NLM @cite_5 @cite_21 . @cite_6 uses a multiscale approach to perform NLM filtering at each level of a Laplacian pyramid. The pull-push NLM @cite_3 method constructs up and down pyramids where NLM weights are fused separately.
@cite_22 @cite_21 @cite_3 @cite_6 @cite_5
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Non-local means filtering (NLM), has garnered a large amount of interest in the image processing community due to its capability to exploit image patch self-similarity in order to effectively filter noisy images. However, the computational complexity of non-local means filtering is the product of three different factors; namely, O(NDK), where K is the number of filter kernel taps (e.g. search window size), D is the number of patch taps, and N is number of pixels. We propose a fast approximation of non-local means filtering using the multiscale methodology of the pull-push scattered data interpolation method. By using NLM with a small filter kernel to selectively propagate filtering results and noise variance estimates from fine to coarse scales and back, the process can be used to provide comparable filtering capability to brute force NLM but with algorithmic complexity that is linear in the number of image pixels and the patch comparison taps, O(ND). In practical application, we demonstrate its denoising capability is comparable to NLM with much larger filter kernels, but at a fraction of the computational cost. This paper presents a simple and robust method, namely discriminative histograms of local dominant orientation (D-HLDO), for biometric image feature extraction. In D-HLDO, the local dominant orientation map and the corresponding relative energy map are obtained by applying the singular value decomposition (SVD) to the collected gradient vectors over a local patch. The dominant orientation map and the relative energy map are then used to construct the concatenated histogram features. Local mean based nearest neighbor discriminant analysis (LM-NNDA) is finally employed to reduce the redundancy information and get the low-dimensional and discriminative features. The proposed method is applied to face, finger-knuckle-print and Palm biometrics and is examined using the AR, CMU PIE and FRGCv2.0 face image databases, the PolyU Palmprint database, and the PolyU Finger-Knuckle-Print database. Experimental results demonstrate the effectiveness of the proposed D-HLDO method. In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually. Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi-stream architecture with convolutional filter supervision and a non-random layer initialization. Experimental results show that our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach.