Low-light image enhancement aims to improve the visual quality of images captured under poor illumination. However, enhancing low-light images often introduces image artifacts, color bias, and low SNR. In this work, we propose AnlightenDiff, an anchoring diffusion model for low light image enhancement. Diffusion models can enhance the low light image to well-exposed image by iterative refinement, but require anchoring to ensure that enhanced results remain faithful to the input. We propose a Dynamical Regulated Diffusion Anchoring mechanism and Sampler to anchor the enhancement process. We also propose a Diffusion Feature Perceptual Loss tailored for diffusion based model to utilize different loss functions in image domain. AnlightenDiff demonstrates the effect of diffusion models for low-light enhancement and achieving high perceptual quality results. Our techniques show a promising future direction for applying diffusion models to image enhancement.

When imaging through a semi-reflective medium such as glass, the reflection of another scene can often be found in the captured images. It degrades the quality of the images and affects their subsequent analyses. In this paper, a novel deep neural network approach for solving the reflection problem in imaging is presented. Traditional reflection removal methods not only require long computation time for solving different optimization functions, their performance is also not guaranteed. As array cameras are readily available in nowadays imaging devices, we first suggest in this paper a multiple-image based depth estimation method using a convolutional neural network (CNN). The proposed network avoids the depth ambiguity problem due to the reflection in the image, and directly estimates the depths along the image edges. They are then used to classify the edges as belonging to the background or reflection. Since edges having similar depth values are error prone in the classification, they are removed from the reflection removal process. We suggest a generative adversarial network (GAN) to regenerate the removed background edges. Finally, the estimated background edge map is fed to another auto-encoder network to assist the extraction of the background from the original image. Experimental results show that the proposed reflection removal algorithm achieves superior performance both quantitatively and qualitatively as compared to the state-of-the-art methods. The proposed algorithm also shows much faster speed compared to the existing approaches using the traditional optimization methods.

Reversible color-to-grayscale conversion (RCGC) is a method that embeds the chromatic information of a full color image into its grayscale version such that the original color image can be reconstructed in the future when necessary. In practical applications, it is required to provide a means to authenticate an information-embedded image such that its integrity can be guaranteed. However, none of the current RCGC algorithms take this factor into account. In this paper, to address this issue, we develop an information-embedding framework based on a vector quantization-based (VQ-based) RCGC algorithm recently proposed by us. Under this framework, we propose a RCGC algorithm that can embed both chromatic information and fragile watermark simultaneously into a grayscale image with the same technique to reduce the complexity and improve the efficiency. Like other VQ-based RCGC algorithms, the performance of the proposed RCGC algorithm highly relies on the palette it uses. We also propose a palette generation algorithm in this paper to support the information embedding process such that the visual quality of the color-embedded grayscale images and the reconstructed color images can be significantly improved.

In daily photography, it is common to capture images in the reflection of an unwanted scene. This circumstance arises frequently when imaging through a semi-reflecting material such as glass. The unwanted reflection will affect the visibility of the background image and introduce ambiguity that perturbs the subsequent analysis on the image. It is a very challenging task to remove the reflection of an image since the problem is severely ill-posed. In this paper, we propose a novel algorithm to solve the reflection removal problem based on light field (LF) imaging. For the proposed algorithm, we first show that the strong gradient points of an LF epipolar plane image (EPI) are preserved after adding to the EPI of another LF image. We can then make use of these strong gradient points to give a rough estimation of the background and reflection. Rather than assuming that the background and reflection have absolutely different disparity ranges, we propose a sandwich layer model to allow them to have common disparities, which is more realistic in practical situations. Then, the background image is refined by recovering the components in the shared disparity range using an iterative enhancement process. Our experimental results show that the proposed algorithm achieves superior performance over traditional approaches both qualitatively and quantitatively. These results verify the robustness of the proposed algorithm when working with images captured from real-life scenes.

In a fringe projection profilometry (FPP) process, the captured fringe images can be modeled as the superimposition of the projected fringe patterns on the texture of the objects. Extracting the fringe patterns from the captured fringe images is an essential procedure in FPP; but traditional single-shot FPP methods often fail to perform if the objects have a highly textured surface. In this paper, a new single-shot FPP algorithm which allows the object texture and fringe pattern to be estimated simultaneously is proposed. The heart of the proposed algorithm is an enhanced morphological component analysis (MCA) tailored for FPP problems. Conventional MCA methods which use a uniform threshold in an iterative optimization process are inefficient to separate fringe-like patterns from image texture. We extend the conventional MCA by taking advantage of the low-rank structure of the fringe's sparse representation to enable an adaptive thresholding process. It ends up with a robust single-shot FPP algorithm that can extract the fringe pattern even if the object has a highly textured surface. The proposed approach has a side benefit that the object texture can be simultaneously obtained in the fringe pattern estimation process, which is useful in many FPP applications. Experimental results have demonstrated the improved performance of the proposed algorithm over the conventional single-shot FPP approaches.

Binary defocusing method is an efficient technique for 3D measurement. It allows real-time measurement by replacing conventional 8-bit fringe patterns with binary patterns. However, there is still room for improving the measurement accuracy of binary defocusing method. This paper presents a novel idea of projecting binary patterns through different color channels of a projector to increase the intensity levels of a fringe pattern without sacrificing the advantages of binary defocusing method. An algorithm is also proposed to optimize fringe patterns for the application. Both simulation and experimental results show that our method can improve the measurement accuracy substantially.

A good halftoning output should bear a blue noise characteristic contributed by isotropically-distributed isolated dots. Multiscale error diffusion (MED) algorithms try to achieve this by exploiting radially symmetric and noncausal error diffusion filters to guarantee spatial homogeneity. In this brief, an optimized diffusion filter is suggested to make the diffusion close to isotropic. When it is used with MED, the resulting output has a nearly ideal blue noise characteristic.

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithm as it can eliminate directional hysteresis completely and possesses good blue noise characteristics. However, due to its filter design, it is not suitable for printing processes which suffer instable dot generation and large dot gain. This paper presents a feature preserving multiscale error diffusion algorithm for green noise digital halftoning. To cope with the edge of the image, the selected diffusion filter is modified according to the edge orientation. Spatial statistics analysis and simulation results show that the proposed algorithm does not only provide outputs of green noise characteristics, but preserve edge details and eliminate pattern artifacts and directional artifacts as well.

In this paper, an efficient edge-directed demosaicing algorithm for Bayer images is presented. A new edge-sensing measure called integrated gradient (IG) is exploited to extract gradient information in both color intensity and color difference domains simultaneously for the interpolations. This measure supports full resolution and allows one to interpolate the missing samples along an appropriate direction. By sharing it in different stages, it guarantees consistency of the interpolation direction in different channels and saves the effort required to repeatedly extracting gradient information from intermediate interpolation results. An IG-based green plane enhancement is also proposed to further improve the method's efficiency. Simulation results confirmed that the proposed demosaicing algorithm outperforms most up-to-date demosaicing algorithms in terms of both quality and complexity.

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it can eliminate directional hysteresis completely and possesses a good blue noise characteristic. However, due to its filter design, it is not suitable for systems with poor isolated dot generation and instable dot gain. In this paper, we propose a MED algorithm to produce halftones of desirable green noise characteristics. This algorithm allows one to adjust the desirable cluster size freely through a single parameter and supports a linear relationship between the cluster size and the input gray level. With a close-to-isotropic diffusion filter, the algorithm can effectively remove pattern artifacts, eliminate directional artifacts and preserve original image details. Analysis and simulation results show that it provides better performance in terms of various aspects including dot distribution, anisotropy and output image quality as compared with other conventional green noise error diffusion algorithms.

In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.

Demosaicing and compression are generally performed sequentially in most digital cameras. Recent reports show that the compression-first scheme outperforms the conventional demosaicing-first scheme in terms of image quality and complexity. In this paper, an efficient lossless compression scheme for Bayer images is presented. It exploits a context matching technique to rank the neighboring pixels for predicting a pixel. Besides, an adaptive color difference estimation scheme is also proposed to remove the spectral redundancy. Simulation results show that the proposed algorithm can achieve a better compression performance as compared with the existing lossless CFA image coding methods.

Color demosaicing and digital zooming are common processes in digital cameras and they often employ similar interpolation concepts based on the information extracted from the raw sensor data. Realizing them independently is not efficient as separate extraction processes are required. It may also cause inconsistent utilization of the raw sensor data in different stages. This paper presents a low-complexity combined algorithm which directly extracts edge information from raw sensor data and exploits it consistently and efficiently in both demosaicing and zooming. The proposed algorithm can produce zoomed full-color images and zoomed CFA images with outstanding performance as compared with conventional approaches.

Adequate definition and adoption of standardized management processes form the foundation to enhance IT service management (ITSM) especially in tally with the arrangement of outsourcing operation management and contracting project development. With the establishment of defacto standard IT infrastructure library (ITIL) for ITSM implementation in these days, management software tools are available in the market. However, proper service management processes shall be developed to align with the existing daily operations workflow, model around ITIL framework, rather than compromising commercial product standard template. This paper first evaluates the effect of ITSM tools in practical adoption in supporting various types of service category in campus environment of technology industries. Experience sharing to address specific concerns in developing customized management process modules for the campus with service outsourcing will be discussed afterwards. Finally, a future work on proactive service impact analysis framework is defined.

This paper presents a low complexity joint color demosaicking and digital zooming algorithm for single-sensor digital cameras. The proposed algorithm directly extracts edge information from raw sensor data for interpolation in both demosaicking and zooming to preserve edge features in its output. This allows the extracted information to be exploited consistently in both stages and also efficiently, as no separate extraction process is required in different stages. The proposed algorithm can produce a zoomed full-color image as well as a zoomed Bayer color filter array image with outstanding performance as compared with conventional approaches which generally combine separate color demosaicking and digital zooming schemes.

In this paper, an adaptive color filter array (CFA) interpolation method is presented. By examining the edge levels and the variance of color difference along different edge directions, the missing green samples are first estimated. The missing red and blue samples are then estimated based on the interpolated green plane. This algorithm can effectively preserve the details as well as significantly reduce the color artifacts. As compared with some current state-of-art methods, the proposed algorithm provides outperformed results in terms of both subjective and objective image quality measures.

Restoration of color-quantized images in which error diffusion is involved is rarely addressed in the literature. Conventional restoration algorithms were generally proposed for handling other noise models and hence they are not effective in restoring color-quantized images. In this paper, we tackle this problem by making use of the best linear estimation. It is shown by the simulation results that the algorithm can improve the quality of color-quantized images to a certain extent

To efficiently render halftone images for various printers and displays that support different resolutions, it is desirable that all halftoning results can be produced at a time, and all of them can be embedded in a single full-scale halftone image such that a simple down-sampling process can extract images of suitable resolutions from it if necessary. This letter presents a framework for achieving this objective with multiscale error diffusion. A multi-resolution halftoning algorithm is proposed based on this framework. Simulation results show that the proposed algorithm can provide good image quality at different resolutions.

This paper presents an adaptive demosaicing algorithm. Missing green samples are first estimated based on the variances of the color differences along different edge directions. The missing red and blue components are then estimated based on the interpolated green plane. This algorithm can effectively preserve the details in texture regions and, at the same time, it can significantly reduce the color artifacts. As compared with the latest demosaicing algorithms, the proposed algorithm produces the best average demosaicing performance both objectively and subjectively.

This paper studies the restoration of images which are color-quantized with error diffusion. Though there are many reported algorithms proposed for restoring noisy blurred color images and inverse halftoning, restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. In this paper, a restoration algorithm based on projection onto convex sets is proposed. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it could improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric.

To reliably and efficiently deliver media information to diverse clients over heterogeneous networks, the media involved must be scalable. In this paper, a color quantization algorithm for generating scalable color-indexed images is proposed based on a multiscale error diffusion framework. Images of lower resolutions are embedded in the outputs such that a simple down-sampling process can extract images of any desirable resolutions. Images possessing this scalable property support transmission over the Internet which contains clients with different display resolutions, systems with different caching resources and networks with varying bandwidths and QoS capabilities. Unlike most of the color halftoning algorithms available nowadays, the proposed algorithm is not dedicated for printing applications but for color-indexed displays. It works with any arbitrary palettes of different size.

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. We propose a POCS-based restoration algorithm to solve the problem. This algorithm derives useful information for restoration with the available color palette and the mechanism of a halftoning process. It is shown to be able to provide a good restoration result.

A new technique for lossy compression of halftone images is proposed based on the vector quantization technique. A conventional vector quantization encoder is modified such that it embeds a block-based error diffusion process and takes a HVS model into account during the compression. This modification significantly improves the visual performance of encoded images while the compression ratio achieved is identical to that of vector quantization.

Restoration of color-quantized images is rarely addressed in the literature, especially when the images are color-quantized with halftoning. Direct applications of existing restoration techniques are generally inadequate to deal with this problem. A restoration algorithm based on simulated annealing is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. It is shown to be able to provide a good restoration result.

Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar.

In fractal coding technique, an image is encoded by making use of its self-similarity property. The image can be reconstructed with some well-defined contractive mappings based on this property and hence, in theory, the reconstructed image can be of any desirable size by using an initial image of appropriate size during the decoding process. However, in practice, the enlarged image is always degraded due to the sub-optimal contractive mappings used. In this paper, a fractal-based image enlargement technique is proposed to reduce this problem. This technique can preserve the details in edge regions while maintaining the smoothness in flat regions, which is superior to conventional image enlargement techniques such as bilinear interpolation and cubic convolution.

Over-complete signal decomposition has been shown to be an efficient technique for coding disparity compensated residual images in stereo image pairs. This paper presents a residual image coding scheme based on a recently proposed MP-based coding scheme called SOSU. The proposed scheme decomposes an orthogonal matching pursuit process into two stages. In different stages, dictionaries of different nature are used. Simulation results show that the proposed scheme can significantly reduce the computation effort while maintaining a similar rate-distortion performance as compared with SOSU.

This paper presents a novel color quantization algorithm for compressed video data. The proposed algorithm extracts discrete cosine (DC) transform coefficients and motion vectors of blocks in a shot to estimate a cumulative color histogram of the shot and, based on the estimated histogram, design a color palette for displaying the video sequence in the shot. It significantly reduces the complexity of the generation of a palette by effectively reducing the number of training vectors used in training a palette without sacrificing the quality. The palette obtained can provide a good display quality even if zooming and panning exist in a shot. The experimental results show that the proposed method can achieve a significant signal-to-noise ratio improvement as compared with conventional video color-quantization schemes when zooming and panning are encountered in a shot.

This paper studies the restoration of color-quantized images. Restoration of color-quantized images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. We propose a POCS-based restoration algorithm specific to color-quantized images, which makes a good use of the available color palette to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a color-quantized image to a certain extent.

This paper presents a novel colour quantization algorithm for compressed video data. The proposed algorithm extracts DC coefficients and motion vectors of blocks in a shot to estimate a cumulative colour histogram of the shot and, based on the estimated histogram, design a colour palette for displaying the video sequence in the shot. It significantly reduces the complexity of the generation of a palette by effectively reducing the number of training vectors used in training a palette without sacrificing the quality. The palette obtained can provide a good display quality even if zooming and panning exist in a shot. The experimental results show that the proposed method can achieve a significant SNR improvement as compared with conventional video colour quantization schemes when zooming and panning are encountered in a shot.

An algorithm for removing impulse noise is proposed. The algorithm not only uses the information of neighboring pixels in a local region, but also uses the long-range correlation within a natural image. In the proposed algorithm, a search criterion based on the weighted transformed contents of regions is used to look for a region which is highly correlated to the region of interest and then the center of that region is selected to replace the corrupted pixel. The method is found to be very effective in removing impulse noise from corrupted images, both in terms of the objective distortion measure and subjective visual assessment.

Matching pursuit has proved to be one of the most efficient techniques in various signal coding techniques. However, its computation effort is so tremendous that it may not be affordable in some real-time applications. A two-stage filtering structure for realizing matching pursuit has recently proposed. This paper presents an efficient algorithm for the implementation of the structure. The issues of how to determine the elementary dictionary and approximate a given initial dictionary are also addressed. Simulation results show that the proposed algorithm can achieve a significant improvement in the complexity-distortion performance.

In conventional model-based coding schemes, predefined static models are generally used. These models cannot adapt to new situations, and hence, they have to be very specific and cannot be generated from a single generic model even though they are very similar. We present a model-generation technique that can gradually build a model and dynamically modify it according to new video frames scanned. The proposed technique is robust to the object's orientation in the view and can be efficiently implemented with a parallel processing technique. As a result, the proposed technique is more attractive to the practical use of model-based coding techniques in real applications.

A digital halftoning method is proposed to diffuse error with a more symmetric error distribution by making use of the concept of multiscale error diffusion. It can improve the diffusion performance by effectively reducing directional hysteresis. The diffusion is row-oriented father than frame-oriented and hence can reduce a lot of computation effort as compared with conventional multiscale error diffusion schemes. This makes it possible for real-time applications.

Matching pursuits is proved to be one of the most efficient techniques used in video coding. However, its computation effort is so tremendous that it may not be affordable in some real-time applications. This paper presents an efficient algorithm for the implementation of matching pursuits. Simulation results show that the computation effort can be successfully reduced with the proposed method.

In this paper, a block-based coding scheme is proposed. Unlike those schemes exploited in a conventional codec, the proposed scheme takes the post-processing scheme to be used in the decoder into account during encoding. An example is also provided in this paper to show how this coding scheme jointly optimizes the compression and the blocking-effect elimination when an MPEG-4 deblocking filter is exploited in the post-processing stage.

In this paper, a digital halftoning method is proposed based on a recently-proposed multiscale error diffusion technique for digital halftoning. It can definitely improve the diffusion performance by effectively removing pattern noise and eliminating boundary and 'blackhole' effects. A dot-overlap compensation scheme is also proposed to eliminate the bias in the gray scale of the printed images.

In conventional model-based coding schemes, predefined static models are generally used. These models cannot adapt to new situations and hence they have to be very specific and cannot be generated from a single generic model even though they are very similar. In this paper, we present a model generation technique that can gradually build a model and dynamically modify it according to new video frames scanned. The proposed technique is robust to the object's orientation in the view and can be efficiently implemented with parallel processing technique. As a result, the proposed technique is more attractive to the practical use of model-based coding techniques in real applications.

In conventional model-based coding schemes, predefined static models are generally used. These models cannot adapt to new situations and hence they have to be very specific and cannot be generated from a single generic model even though they are very similar. In this paper, we present a model generation technique that can gradually build a model and dynamically modify it according to new video frames scanned. The proposed technique is robust to the object's orientation in the view and can be efficiently implemented with parallel processing technique. As a result, the proposed technique is more attractive to the practical use of model-based coding techniques in real applications.

Though image coding techniques that employ subsets of nonorthogonal basis images chosen from two or more transform domains have been shown consistently to yield higher image quality than those based on one transform for a fixed compression ratio, they have not been widely employed due to their very high computational complexity of existing realization approaches. This paper presents a new realization approach for mixed-transform image representation. Computational complexity can be greatly reduced compared with existing approaches.

A digital halftoning method is proposed based on a multiscale error diffusion technique for digital halftoning. It can improve the diffusion performance by effectively removing pattern noise and eliminating boundary and "blackhole" effects. A dot overlap compensation scheme is also proposed to eliminate the bias in the gray scale of the printed images.

A noniterative postprocessing method is proposed to restore the images encoded with block-based compression standards such as Joint Photographic Experts Group (JPEG). This method classifies small local boundary regions according to their intensity distribution and then selects appropriate linear predictors to estimate the corresponding boundary pixels in the regions. This approach is easy to implement and we found in our simulations that its restoration performance was very respectable compared with the reported postprocessing methods.

This paper presents a new restoration approach to reduce coding artifacts in block-transform image coding. Different from conventional restoration techniques, the proposed one is non-iterative and requires a low computational cost, yet can reconstruct objectively and subjectively better images. This good performance is achieved because of the following advantages the proposed approach has: (i) efficient incorporation of the solution bound into restoration; and (ii) effective exploitation of local image properties and statistical knowledge about the quantizers used.

This letter presents a new approach to reduce coding artifacts in transform image coding. We approach the problem in an estimation of each transform coefficient from its quantized version with its local mean and variance. The proposed method can significantly reduce coding artifacts of low bit-rate coded images, and at the same time guarantee that the resulting images satisfies the quantization error constraint.

The field of image restoration lacks promising comparison vehicle for judging the effectiveness of competing algorithms. By far the most widely adopted quantitative measure of image restoration quality is the SNR improvement. However, we find that the SNR improvement is of low precision, which will adversely hinder it from being a reliable measure. It is also noted that another limitation of the SNR improvement is that it cannot reveal clearly the extent to which the image quality is improved. We devise an alternative measure for quantitative evaluation of image restoration quality. The proposed measure is much more precise than the SNR improvement. Moreover, the proposed measure contains finite and meaningful reference points in its measurements, to provide us with a better insight into the effectiveness of restoration algorithms that under study.

In this paper, a non-iterative post-processing method is proposed to restore the images encoded with block-based compression standards such as JPEG. This method classifies small local boundary regions according to their intensity distribution and then select appropriate linear predictors to estimate the corresponding boundary pixels in the regions. This approach is easy to implement and we found in our simulations that its restoration performance was very respectable compared with the reported post-processing methods.

The discrete cosine transform (DCT) is widely used in digital signal processing. It is always desirable to look for more efficient algorithms for the realization of the DCT. We generalize a formulation for converting a length-2/sup n/ DCT into n groups of equations, then apply a novel technique for its implementation. The sizes of the groups are 2/sup m/, for m=n-1,...,0. While their structures are extremely regular. The realization can then be converted into the simplest recursive filter form, which is of particularly simple for practical implementation. The filter structure is numerically stable, since it involves no division at all.

In this paper, adaptive coding schemes for contour line drawings based on chain code representation is presented. In this scheme, the chain code or the chain-difference code of a contour is modeled as an n-order Markov sequence and then coded with an arithmetic coding scheme adaptively. Experimental result shows that the proposed approach is better than some other conventional approaches.

This paper presents two new approaches to remove blocking effect in low-bit rate transform coded images by using subband decomposition/reconstruction technique. They are designed to act as a supplementary post-processing step of the JPEG standard. Both approaches make use of the noise characteristic of each subband to bound the maximum tolerable error and the smoothness of the restored images in restoring subband images with regularization. One of them will also utilize the spatial activity of the restoring images to tighten the bounds. Computer simulations showed that the new adaptive objective functions could achieve a better restoration performance in terms of both subjective and objective measures than did other conventional objective functions.

This paper presents a new adaptive objective function based on the regularized iterative block reduction technique for low-bit rate transform coded images. Also, a better initial estimate for the regularization approach is presented. Two types of prior knowledge are used: the first type bounds the maximum tolerable error (roughness), and the second type restricts the high-frequency content (smoothness) of the restorated images. Computer simulations showed that the new adaptive objective function with the proposed initial estimate performed better on both subjective and objective measures than did a previously proposed objective function.

The codeword searching sequence is sometimes very vital to the efficiency of a vector quantization (VQ) encoding algorithm. In this paper, we evaluate some necessary criteria for the derivation of an optimal searching sequence and derive the optimal searching sequence based on such criteria.

It has been shown in the literature that adaptive regularized image restoration is superior to the non-adaptive case. However, the adaptivity introduced in most proposed iterative algorithms is based only on the application of the space-variant smoothing operator. It is found that these adaptive algorithms suffer from insufficient smoothing of the flat image regions. In this paper, an adaptive iterative image restoration algorithm, which applies both techniques of space-variant smoothing and space-variant restoration, is proposed to overcome the stated problem. It is shown by experiments that the restored images obtained by the proposed algorithm are better in terms of both numerical measurement and visual quality.>

Most VQ encoding algorithms are proposed to reject codewords or stop searching codewords as soon as possible by using various criteria. This paper proposes an algorithm to dynamically determine the codeword searching sequence for a given input vector, which can further improve the codeword elimination efficiency. An encoding algorithm is also proposed based on this idea.>

In this paper, the starting and ending position indices for far-end echo are estimated. A search algorithm is proposed to determine the position index using the centroid of the dispersive region as the initial starting point. Block processing algorithms are proposed for the smoothing of the estimated dispersive length. The results show that improvement can be obtained by increasing the block length. Further improvement can be obtained by using the instantaneous block LMS weight updating in the coefficient of echo canceller.>

We generalize a formulation for converting a length-2/sup n/ discrete cosine transform into n groups of equations, then apply a novel technique for its implementation. The sizes of the groups are 2/sup n-1/, 2/sup n-2/, ...2/sup 0/ respectively, while their structures are extremely regular. The realization can then be converted into recursive filter form, which is particularly simple for practical implementation.>