Scale-space and edge detection using anisotropic diffusion

The scale-space technique introduced by Witkin involves generating coarser resolution images by convolving the original image with a Gaussian kernel. This approach has a major drawback: it is difficult to obtain accurately the locations of the “semantically meaningful” edges at coarse scales. In this paper we suggest a new definition of scale-space, and introduce a class of algorithms that realize it using a diffusion process. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing in preference to interregion smoothing. It is shown that the “no new maxima should be generated at coarse scales” property of conventional scale space is preserved. As the region boundaries in our approach remain sharp, we obtain a high quality edge detector which successfully exploits global information. Experimental results are shown on a number of images. The algorithm involves elementary, local operations replicated over the image making parallel hardware implementations feasible

Speckle Noise Reduction using Local Binary Pattern

AbstractA novel local binary pattern (LBP) based adaptive diffusion for speckle noise reduction is presented. The LBP operator unifies traditionally divergent statistical and structural models of region analysis. We use LBP textons to classify an image around a pixel into noisy, homogenous, corner and edge regions. According to different types of regions, a variable weight is assigned in to the diffusion equation, so that our algorithm can adaptively encourage strong diffusion in homogenous/noisy regions and less on the edge/corner regions. The diffusion preserves edges, local details while diffusing more on homogenous region. The experiments results are evaluated both in terms of objective metric and the visual quality

An adaptive noise removal approach for restoration of digital images corrupted by multimodal noise

Data smoothing algorithms are commonly applied to reduce the level of noise and eliminate the weak textures contained in digital images. Anisotropic diffusion algorithms form a distinct category of noise removal approaches that implement the smoothing process locally in agreement with image features such as edges that are typically determined by applying diverse partial differential equation (PDE) models. While this approach is opportune since it allows the implementation of feature-preserving data smoothing strategies, the inclusion of the PDE models in the formulation of the data smoothing process compromises the performance of the anisotropic diffusion schemes when applied to data corrupted by non-Gaussian and multimodal image noise. In this paper we first evaluate the positive aspects related to the inclusion of a multi-scale edge detector based on the generalisation of the Di Zenzo operator into the formulation of the anisotropic diffusion process. Then, we introduce a new approach that embeds the vector median filtering into the discrete implementation of the anisotropic diffusion in order to improve the performance of the noise removal algorithm when applied to multimodal noise suppression. To evaluate the performance of the proposed data smoothing strategy, a large number of experiments on various types of digital images corrupted by multimodal noise were conducted.Keywords — Anisotropic diffusion, vector median filtering, feature preservation, multimodal noise, noise removal

Edge-Aware Spatial Denoising Filtering Based on a Psychological Model of Stimulus Similarity

Noise reduction is a fundamental operation in image quality enhancement. In recent years, a large body of techniques at the crossroads of statistics and functional analysis have been developed to minimize the blurring artifact introduced in the denoising process. Recent studies focus on edge-aware filters due to their tendency to preserve image structures. In this study, we adopt a psychological model of similarity based on Shepard’s generalization law and introduce a new signal-dependent window selection technique. Such a focus is warranted because blurring is essentially a cognitive act related to the human perception of physical stimuli (pixels). The proposed windowing technique can be used to implement a wide range of edge-aware spatial denoising filters, thereby transforming them into nonlocal filters. We employ simulations using both synthetic and real image samples to evaluate the performance of the proposed method by quantifying the enhancement in the signal strength, noise suppression, and structural preservation measured in terms of the Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) index, respectively. In our experiments, we observe that incorporating the proposed windowing technique in the design of mean, median, and nonlocalmeans filters substantially reduces the MSE while simultaneously increasing the PSNR and the SSIM

Pixel area variations in sensors: a novel framework for predicting pixel fidelity and distortion in flat field response

We describe the drift field in thick depleted silicon sensors as a superposition of a one-dimensional backdrop field and various three-dimensional perturbative contributions that are physically motivated. We compute trajectories for the conversions along the field lines toward the channel and into volumes where conversions are confined by the perturbative fields. We validate this approach by comparing predictions against measured response distributions seen in five types of fixed pattern distortion features. We derive a quantitative connection between "tree ring" flat field distortions to astrometric and shape transfer errors with connections to measurable wavelength dependence - as ancillary pixel data that may be used in pipeline analysis for catalog population. Such corrections may be tested on DECam data, where correlations between tree ring flat field distortions and astrometric errors - together with their band dependence - are already under study. Dynamic effects, including the brighter-fatter phenomenon for point sources and the flux dependence of flat field fixed pattern features are approached using perturbations similar in form to those giving rise to the fixed pattern features. These in turn provide drift coefficient predictions that can be validated in a straightforward manner. Once the three parameters of the model are constrained using available data, the model is readily used to provide predictions for arbitrary photo-distributions with internally consistent wavelength dependence provided for free.Comment: 17 pages, 7 figures, submitted to "Precision Astronomy with Fully Depleted CCDs" - conference proceedings to be published by JINS