We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality.

A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

We propose an effective video denoising method based on highly sparse signal representation in local 3D transform domain. A noisy video is processed in blockwise manner and for each processed block we form a 3D data array that we call “group” by stacking together blocks found similar to the currently processed one. This grouping is realized as a spatio-temporal predictive-search block-matching, similar to techniques used for motion estimation. Each formed 3D group is filtered by a 3D transform-domain shrinkage (hard-thresholding and Wiener filtering), the result of which are estimates of all grouped blocks. This filtering — that we term “collaborative filtering” — exploits the correlation between grouped blocks and the corresponding highly sparse representation of the true signal in the transform domain. Since, in general, the obtained block estimates are mutually overlapping, we aggregate them by a weighted average in order to form a non-redundant estimate of the video. Significant improvement of this approach is achieved by using a two-step algorithm where an intermediate estimate is produced by grouping and collaborative hard-thresholding and then used both for improving the grouping and for applying collaborative empirical Wiener filtering. We develop an efficient realization of this video denoising algorithm. The experimental results show that at reasonable computational cost it achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

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Video denoising by sparse 3D transform-domain collaborative filtering

[서평]「Digital Video Processing」

In this paper, we consider denoising of image sequences that are corrupted by zero-mean additive white Gaussian noise. Relative to single image denoising techniques, denoising of sequences aims to also utilize the temporal dimension. This assists in getting both faster algorithms and better output quality. This paper focuses on utilizing sparse and redundant representations for image sequence denoising. In the single image setting, the K-SVD algorithm is used to train a sparsifying dictionary for the corrupted image. This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations. These modifications lead to substantial benefits in complexity and denoising performance, compared to simply running the single image algorithm sequentially. The algorithm's performance is experimentally compared to several state-of-the-art algorithms, demonstrating comparable or favorable results.

Image Sequence Denoising via Sparse and Redundant Representations

An image processing apparatus includes a plurality of addition means and an image processing means. The addition means performs addition processing of adding pixels of a differential image at a second resolution representing a difference between an inputted image at a first resolution and an image at the second resolution higher than the first resolution as pixels of an inputted image at the second resolution. The image processing means is configured to perform second and subsequent addition processing, and generate an image of the second resolution as a processing result by performing the addition processing for a predetermined number of times. The addition processing is performed with inputs of an image at the first resolution and an image at the second resolution obtained by an immediately preceding addition processing, which are different from each other.

Image processing apparatus, image processing method and program

In this work, we consider the adaptive Wiener filtering of noisy images and image sequences. We begin by using an adaptive weighted averaging (AWA) approach to estimate the second-order statistics required by the Wiener filter. Experimentally, the resulting Wiener filter is improved by about 1 dB in the sense of peak-to-peak SNR (PSNR). Also, the subjective improvement is significant in that the annoying boundary noise, common with the traditional Wiener filter, has been greatly suppressed. The second, and more substantial, part of this paper extends the AWA concept to the wavelet domain. The proposed AWA wavelet Wiener filter is superior to the traditional wavelet Wiener filter by about 0.5 dB (PSNR). Furthermore, an interesting method to effectively combine the denoising results from both wavelet and spatial domains is shown and discussed. Our experimental results outperform or are comparable to state-of-art methods.

Adaptive Wiener filtering of noisy images and image sequences

This paper develops a new approach to video denoising, in which motion estimation/compensation, temporal filtering, and spatial smoothing are all undertaken in the wavelet domain. The key to making this possible is the use of a shift-invariant, overcomplete wavelet transform, which allows motion between image frames to be manifested as an equivalent motion of coefficients in the wavelet domain. Our focus is on minimizing spatial blurring, restricting to temporal filtering when motion estimates are reliable, and spatially shrinking only insignificant coefficients when the motion is unreliable. Tests on standard video sequences show that our results yield comparable PSNR to the state of the art in the literature, but with considerably improved preservation of fine spatial details.

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Wavelet video denoising with regularized multiresolution motion estimation

Filter bank design for wavelet compression is crucial; careful design enables superior quality for broad classes of images. The Bernstein basis for frequency-domain construction of biorthogonal nearly coiflet (BNC) wavelet bases forms a unified design framework for high-performance medium-length filters. A common filter bandwidth is characteristic of widely favoured BNC filter pairs: the classical CDF 9/7, the Villasenor 6/10, and the Villasenor 10/18. Based on this observation, we construct previously unknown BNC 17/11 and BNC 16/8 wavelet filters. Key filter-quality evaluation metrics, due to Villasenor, demonstrate these filters to be well suited for image compression. Also studied are the biorthogonal coiflet 17/11 (half-band), 18/10 and 10/6 filter pairs, which have not previously been formally evaluated for image coding. Simulation results confirm that the BNC 17/11 and BNC 16/8 wavelet bases are outstanding for compression of natural and medical images, and particularly for images with significant high-frequency detail, such as fingerprints. The BNC 17/11 pair recommends itself for international standardization for the compression of still images; the BNC 16/8 pair for high-quality compression of production quality video. Experimental evidence suggests biorthogonal filters achieve good compression if, subject to a filter bandwidth constraint, maximum vanishing moments are obtained for a given filter support.

Biorthogonal nearly coiflet wavelets for image compression

There is wide application for the extraction of textual information from low-contrast, complex natural images. We are particularly interested in segmentation and thresholding algorithms for use in a portable text-to-speech system for the vision impaired. Reading low-contrast LCD displays is the target application. We present a low-complexity method for automatically extracting text of any size, font, and format from images acquired by a video camera that may be poorly focused and aimed, under conditions of inadequate and uneven illumination. The new method consists of fast thresholding that combines a local variance measure with a logical stroke-width method, and with a low-complexity statistical and contextual noise segmentation. The performance of this method compares favorably with more complex methods for the extraction of characters from scene images. Initial results are encouraging for application in a robust portable reader.

Low-complexity character extraction in low-contrast scene images

We are developing a portable text-to-speech system for the vision impaired. The input image is acquired with a lightweight CCD camera that may be poorly focused and aimed, and perhaps taken under inadequate and uneven illumination. We therefore require efficient and effective thresholding and segmentation methods which are robust with respect to character contrast, font, size, and format. In this paper, we present a fast thresholding scheme which combines a local variance measure with a logical stroke-width method. An efficient post- thresholding segmentation scheme utilizing Fisher's linear discriminant to distinguish noise and character components functions as an effective pre-processing step for the application of commercial segmentation and character recognition methods. The performance of this fast new method compared favorably with other methods for the extraction of characters from uncontrolled illumination, omnifont scene images. We demonstrate the suitability of this method for use in an automated portable reader through a software implementation running on a laptop 486 computer in our prototype device.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Lowell L. Winger

Papers

Adaptive Wiener filtering of noisy images and image sequences

Wavelet video denoising with regularized multiresolution motion estimation

Biorthogonal nearly coiflet wavelets for image compression

Low-complexity character extraction in low-contrast scene images

Character segmentation and thresholding in low-contrast scene images