Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

Adaptive morphological and bilateral filtering with ensemble convolutional neural network for pose-invariant face recognition

Abstract: Based on behavioural or physical characteristics, humans are recognized by using biometric system. In computer vision and pattern recognition domain, dynamic research is going on in face recognition. Face recognition algorithms are challenged by intra-personal changes in pose, illumination, and expression (PIE). Images are processed and matched with various databases. For face recognition, multi-task learning (MTL) is explored in this work. However, recognition of faces from blur and poor illumination becomes difficult. Recovering face from mixed noise degradation is a challenging and promising theme. This work explores an ensample convolutional neural network (ECNN) for face recognition. Initially a new adaptive morphological bilateral filtering (AMBF) method is proposed. Without introducing undershoot or overshoot, slope of edges is increased for sharpening a blur image. Quality sharpening enhancement is assured by various morphological operations like closing, opening, erosion and dilation with proper size of structure element. In addition to adaptive bilateral filter, mathematical morphology operations are included to enhance the performance. Then a multi-task ECNN is implemented for a main classification task and estimation of pose, blur, illumination, and expression (PBIE) as side tasks. For every side task, loss weights are assigned automatically by developing bat algorithm (BA) based dynamic-weighing method. In multi-task ECNN, balance between various tasks are achieved. Hence, proposed method is effectively demonstrated by the results of experimentation on entire multi-PIE dataset.

Bilateral despeckling filter in homogeneity domain for breast ultrasound images

Abstract: Breast sonograms are more effective towards differentiation of cysts from solid tumours; if they could be post-processed for minimization of speckle content without blurring of edges. The approach presented in this paper consists of a bilateral filtering in homogeneity domain so that the despeckling process do not compromises the texture and features of masses. The proposed despeckled approach decomposes the input image into homogeneous and non-homogeneous regions; which are then selectively processed using the bilateral filter. The domain filtering component is made dominant when applied to homogeneous pixels providing smoothening while the range filter dominates on the non-homogeneous pixels leading to edge preservation. Simulations carried out on breast ultrasound images depict satisfactory speckle filtering supported with improvement in values of performance parameters (PSNR, SSIM & SSI).

Sparse PDF maps for non-linear multi-resolution image operations

Abstract: We introduce a new type of multi-resolution image pyramid for high-resolution images called sparse pdf maps (sPDF-maps). Each pyramid level consists of a sparse encoding of continuous probability density functions (pdfs) of pixel neighborhoods in the original image. The encoded pdfs enable the accurate computation of non-linear image operations directly in any pyramid level with proper pre-filtering for anti-aliasing, without accessing higher or lower resolutions. The sparsity of sPDF-maps makes them feasible for gigapixel images, while enabling direct evaluation of a variety of non-linear operators from the same representation. We illustrate this versatility for antialiased color mapping, O(n) local Laplacian filters, smoothed local histogram filters (e.g., median or mode filters), and bilateral filters.

Method and apparatus for reducing ringing artifacts

Abstract: A method and apparatus for ringing artifacts reduction for compressed video signals. The method includes receiving luma data to the digital signal processor, calculating sum of gradient of the luma data; calculating SAD of the luma data; performing pixel classification based of the calculated SAD and sum of gradient, performing erosion on a detected edge pixel indicator on a detected flat pixel indicators, determining at least one of the strength or weakness of the an edge based on the determined edge erosion, performing horizontal dilation on the detected edge pixel indicators and edge strength; and performing at least one of sigma or bilateral filtering to the luma data according to the detected edge pixel indicator, flat pixel indicator, edge strength, the number of very flat pixel in the block.