Abstract
The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.
Highlights
Image denoising is an important and fundamental issue in the field of computer low-level vision and has received considerable attention from scholars and practitioners. e goal of such task is to remove noise while preserving potential image features and further acquire a clean image which can be reliably used for subsequent vision tasks such as edge detection and object segmentation and fusion.In the last two decades, the topic for image denoising has been well studied in the literature
We aim to address these drawbacks by introducing a directional local variance (DLV) metric and develop an image scale-space filter based on anisotropic diffusion by simultaneously utilizing the spatial gradient and DLV metric for image denoising
We conduct several experiments to assess the effectiveness of the proposed filter and compare it with some competitive benchmark models: local variance-based PM model (LV-PM) [21], Dynamics in Nature and Society α-PM (D-α-PM) model [26], and recent fourth-order anisotropic diffusion model (FAD) [30]
Summary
Image denoising is an important and fundamental issue in the field of computer low-level vision and has received considerable attention from scholars and practitioners. e goal of such task is to remove noise while preserving potential image features and further acquire a clean image which can be reliably used for subsequent vision tasks such as edge detection and object segmentation and fusion.In the last two decades, the topic for image denoising has been well studied in the literature (see, e.g., the work of Tomasi and Manduchi [1], Buades et al [2], Dabov et al [3], Zhang et al [4], He et al [5], Zuo et al [6], Zhang et al [7], and Dong et al [8] and references therein). Apart from the mentioned methods, a kind of anisotropic diffusion models based on nonlinear partial differential equations are attractive and effective for image denoising. Anisotropic diffusion models are still popular in the academic community, e.g., Chen [19] proposes an adaptive smoothing via local and contextual discontinuities. Chen et al [20] present the ramp preserving PM model which is an effective tool for ramp preservation and speckle reduction. Chao and Tsai [21] and Li et al [22] show that the local gray-level variance is an effective edge indicator for detecting image features by which they propose their modified PM models for feature preservation, respectively. Lefkimmiatis et al [25] formulate the regularization model via Hessian-based norm for medical image denoising. Guo et al [26] design an adaptive PM model called the dynamic
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