Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.

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Bilateral filtering for gray and color images

国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

New edge-directed interpolation

In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its applications. The construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a flexible multiscale, multidirection, and shift-invariant image decomposition that can be efficiently implemented via the a trous algorithm. At the core of the proposed scheme is the nonseparable two-channel nonsubsampled filter bank (NSFB). We exploit the less stringent design condition of the NSFB to design filters that lead to a NSCT with better frequency selectivity and regularity when compared to the contourlet transform. We propose a design framework based on the mapping approach, that allows for a fast implementation based on a lifting or ladder structure, and only uses one-dimensional filtering in some cases. In addition, our design ensures that the corresponding frame elements are regular, symmetric, and the frame is close to a tight one. We assess the performance of the NSCT in image denoising and enhancement applications. In both applications the NSCT compares favorably to other existing methods in the literature

/pdf/the-nonsubsampled-contourlet-transform-theory-design-and-3a4lsit6lx.pdf

The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

This paper presents a new method for unsharp masking for contrast enhancement of images. The approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.

https://www.eecis.udel.edu/~barner/courses/eleg675/papers/Enhancement%20in%20the%20Spatial%20Domain/Image%20Enhancement%20via%20Adaptive%20Unsharp%20Masking.pdf

Image enhancement via adaptive unsharp masking

A new operator is presented which adopts a fuzzy logic approach for the enhancement of images corrupted by impulse noise. The proposed operator is based on two-step fuzzy reasoning, and it is able to perform a very strong noise cancellation while preserving image details very well. The new fuzzy filter is favorably compared with other nonlinear operators in the literature.

A fuzzy filter for images corrupted by impulse noise

In the unsharp masking approach for image enhancement,
a fraction of the highpass filtered version of the image is added to the original image to form the enhanced version The method is simple, but it suffers from two serious drawbacks First, it enhances the contrast in the darker areas perceptually much more strongly than that in the lighter areas Second, it enhances the noise and/or digitization effects, particularly in the darker regions, resulting in visually less pleasing enhanced images In general, noise can be suppressed with lowpass filters, which are associated with the blurring of the edges On the other hand, contrast can be enhanced with highpass filters, which are associated with noise amplification A reasonable solution, therefore, is to use suitable nonlinear filters which combine the features of both highpass and lowpass filters This paper outlines several new methods of unsharp masking based on the use of such nonlinear filters Computer simulations have verified the superior results obtained using these filters In addition, a new measure of contrast enhancement is introduced which quantitatively supports the improvement obtained using the proposed methods

Nonlinear unsharp masking methods for image contrast enhancement

A cubic unsharp masking technique for contrast enhancement

A frame interpolation algorithm for frame rate up-conversion of progressive image sequences is proposed. The algorithm is based on simple motion compensation and linear interpolation. A motion vector is searched for each pixel in the interpolated image and the resulting motion field is median filtered to remove inconsistent vectors. Averaging along the motion trajectory is used to produce the interpolated pixel values. The main novelty of the proposed method is the motion compensation algorithm which has been designed with low computational complexity as an important criterion. Subsampled blocks are used in block matching and the vector search range is constrained to the most likely motion vectors. Simulation results show that good visual quality has been obtained with moderate complexity. The algorithm has been designed mainly for 50 Hz to 75 Hz frame rate up-conversion with applications in a multimedia environment, but it can also be used in advanced television receivers to remove artifacts due to low scan rate.

Giovanni Ramponi

Papers

Image enhancement via adaptive unsharp masking

A fuzzy filter for images corrupted by impulse noise

Nonlinear unsharp masking methods for image contrast enhancement

A cubic unsharp masking technique for contrast enhancement

A method for motion adaptive frame rate up-conversion