Techniques and tools for conversion operations between modules in a scalable video encoding tool or scalable video decoding tool are described. For example, given reconstructed base layer video in a low resolution format (e.g., 4:2:0 video with 8 bits per sample) an encoding tool and decoding tool adaptively filter the reconstructed base layer video and upsample its sample values to a higher sample depth (e.g., 10 bits per sample). The tools also adaptively scale chroma samples to a higher chroma sampling rate (e.g., 4:2:2). The adaptive filtering and chroma scaling help reduce energy in inter-layer residual video by making the reconstructed base layer video closer to input video, which typically makes compression of the inter-layer residual video more efficient. The encoding tool also remaps sample values of the inter-layer residual video to adjust dynamic range before encoding, and the decoding tool performs inverse remapping after decoding.

Conversion operations in scalable video encoding and decoding

A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise

We present a new upsampling method to enhance the spatial resolution of depth images. Given a low-resolution depth image from an active depth sensor and a potentially high-resolution color image from a passive RGB camera, we formulate it as an adaptive cost aggregation problem and solve it using the bilateral filter. The formulation synergistically combines the median and bilateral filters thus it better preserves the depth edges and is more robust to noise. Numerical and visual evaluations on a total of 37 Middlebury data sets demonstrate the effectiveness of our method. A real-time high-resolution depth capturing system is also developed using commercial active depth sensor based on the proposed upsampling method.

Fusion of Median and Bilateral Filtering for Range Image Upsampling

Image Denoising is one of the fundamental and very important necessary processes in image processing. It is still a challenging and a hot problem for researchers. Images are one of essential representations in every field like education, agriculture, geosciences, aerospace, surveillance, entertainment etc by means of electronic or print media. Images can get corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods have been proposed. An overview of various methods is given here after a brief introduction. These methods have been categorized on the bases of techniques used.

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A Review of Image Denoising Methods

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.

/pdf/adaptive-two-pass-rank-order-filter-to-remove-impulse-noise-1c9vd9q811.pdf

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

This paper describes the definition and testing of a new type of median filter for images. The topological median filter implements some existing ideas and some new ideas on fuzzy connectedness to improve, over a conventional median filter, the extraction of edges in noise. The concept of /spl alpha/-connectivity is defined and used to create an algorithm for computing the degree of connectedness of a pixel to all the other pixels in an arbitrary neighborhood. The resulting connectivity map of the neighborhood effectively disconnects peaks in the neighborhood that are separated from the center pixel by a valley in the brightness topology. The median of the connectivity map is an estimate of the median of the peak or plateau to which the center pixel belongs. Unlike the conventional median filter, the topological median is relatively unaffected by disconnected features in the neighborhood of the center pixel. Four topological median filters are defined. Qualitative and statistical analyses of the four filters are presented. It is demonstrated that edge detection can be more accurate on topologically median filtered images than on conventionally median filtered images.

R.A. Peters

Papers

Topological median filters