Fast Image Restoration for Reducing Block Artifacts Based on Adaptive Constrained Optimization

Blocking effect reduction of compressed images using classification-based constrained optimization

This paper proposes a novel blocking map guided fuzzy filter to remove blocking artifacts in compressed images. The fuzzy filter is controlled adaptively by a blocking artifact map. The blocking artifact map is built on local characteristics of the image content. The masking effect of human visual system (HVS) is also considered to estimate the blocking artifact level more accurately. Experimental results show that blocking artifact map fits well to the visual system. The proposed deblocking method has better detail preservation and lower computational costs than other methods. It achieves better performance in both reducing noise and maintaining better texture detail between block borders than the other algorithms.

In this paper, a new technique for acceleration of iterative image restoration algorithms is presented. Unlike other fast algorithms which tend to accelerate the rate of convergence of iterative procedures, the proposed method achieves the speed improvement by extending the equivalent restoration filter of a small local block to the whole image directly. Considerable reduction in restoration time can be achieved with little image distortion. The speedup achieved increases rapidly with the drop of the ratio of the block size to the whole image size. An expression for estimating the acceleration factor is derived and confirmed experimentally. The proposed method has a special application in restoration of very large digital images with the prevalence of digital cameras.

The Medium Resolution Spectral Imager (MERSI) was installed as a key payload on the FengYun-3C (FY-3C) polar-orbit meteorological satellite, which was successfully launched on September 23, 2013. We used 14 months of continuous FY-3C/MERSI Level-1B data (from November 2013 to December 2014) to evaluate the on-orbit image spatial quality. Based on a polar ice block image, a sharp target modulation transfer function (MTF) estimation method is used to quantitatively estimate the MTF value at the Nyquist frequency, which is an index for the spatial quality. The results show very good stability in the first year and a relatively lower spatial quality (MTF approximately 0.15) along the FY-3C/MERSI scan direction. This lower spatial quality is primarily attributed to the known and fixed 27% overlapped scan mode of MERSI, which can significantly reduce the image contrast. By using this fixed overlapped proportion (27%), we develop a fast and robust image restoration algorithm based on the Gaussian elimination (GE) method with lower and upper triangular matrix decomposition (LU). The speed-up ratio of this GE with LU decomposition method can attain a value of 626.30 compared with the traditional GE method when it solves linear equations with 2048 MERSI scan pixels. After the image restoration process, significant enhancement in the image spatial quality along the scan direction for every band of FY-3C/MERSI can be found with an increased MTF value of approximately 0.30. However, we evaluate the possible effect of this restoration algorithm on the original digital number (DN) and reflectance values. We find a slight decrease in the total averaged DN (0.5) and reflectance (< 0.5%, relative bias) values. The variation in DN or reflectance after the image restoration process exhibits a positive correlation with homogeneity of the original target. Moreover, a sensitivity study on the reflectance reveals that it has a more significant impact on the inhomogeneous pixel with a low DN value.

The theory of projection onto convex sets (POCS) is applied to reduce blocking artifacts in compressed images coded by the block discrete cosine transform (BDCT). An image before compression is simulated by a triangular mesh, which is the basis of a proposed smoothness constraint set. The mesh is constructed by dividing each block into a set of triangles. The proposed method outperforms four existing methods subjectively and objectively.

Detecting edge existence and analyzing the edge direction in a block level is useful in the applications, such as perceptually improved image processing, blocking artifacts reduction, content-based retrieval, senses changing detection, etc. This paper proposes a novel Fast Block Edge Direction Analysis method. The method is derived from a simple pixel domain in a deductive way. Then we demonstrate how to calculate the key parameters of the method with DCT coefficients. With this method, one block is classified into one of the 17 category edges: No edge plus 16 different edge patterns. The calculation of the key parameters can be calculated with extremely low complexity both in pixel domain and in DCT domain.

Image inpainting is not only the art of restoring damaged images but also a powerful technique for image editing e.g. removing undesired objects, recomposing images, etc. Recently, it becomes an active research topic in image processing because of its challenging aspect and extensive use in various real-world applications. In this paper, we propose a novel efficient approach for high-quality and fast image restoration by combining a greedy strategy and a global optimization strategy based on a pyramidal representation of the image. The proposed approach is validated on different state-of-the-art images. Moreover, a comparative validation shows that the proposed approach outperforms the literature in addition to a very low complexity.KeywordsHierarchical ApproachGreedy StrategyImage EditingImage InpaintingActive Research TopicThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Summary form only given. Block-based Discrete Cosine Transform (BDCT) has been widely used in image and video compression due to its energy compacting property and relative ease of implementation. However, BDCT has a major drawback, which is usually referred to as blocking artifacts. Blocking artifacts appear as grid noise along the block boundaries because each block is transformed and quantized independently. Image deblocking techniques can reduce these distortions and alleviate the conflict between bit rate reduction and visual quality preservation. Many state-of-the-art image deblocking algorithms treated the blocking artifacts reduction of the compressed image as an inverse restoration problem. Natural image prior models are well utilized into the blocking artifacts reduction processing, such as the local sparsity prior model and non-local similarity property of natural images. These two local and non-local models characterize the image prior information in two complementary perspectives. Therefore, it is necessary to combine these two models in a unified framework. In this paper, we propose a novel method to reduce the blocking artifacts of blockcoded images via patch clustering and low-rank minimization, which simultaneously exploits the local and non-local sparse representations in a unified framework. First, the whole compressed image are divided into small patches. For each patch, we perform patch clustering to collect similar patches into a group. Then the whole group are simultaneously reconstructed by a low-rank minimization approach. Singular value thresholding (SVT) algorithm is employed to solve the low-rank minimization problem. To further improve the performance of the proposed algorithm, we adopt an iterative procedure to utilize the newly output data in each iteration and update the noise and signal variance adaptively. Experimental results show that the proposed method achieves higher PSNR and SSIM than the state-of-the-art methods. Comparing to the state-of-theart algorithms and, the proposed algorithm achieves about 0.37dB and 0.11dB improvement on average. For visual quality assessment, the deblocking images produced by the proposed algorithm reveal much more sharp edge structures and richer textures.

Block based transform coding is the most popular technique for image and video compression. However, images compressed using block based algorithms exhibit blocking which is one of the most annoying artifacts in compressed images and video. Many deblocking algorithms have been proposed to improve the quality of the reconstructed image. Although the deblocking operation improves the image appearance by smoothing the block discontinuities, a loss of information also occurs during this process. As a consequence, there is a need to develop methods to reduce the blocking artifacts while minimizing the information loss. We propose a novel fusion based technique to minimize the loss of useful information which may result from the deblocking operation. The loss of key image information can be reduced by fusing a deblocking result and the compressed image.

Proposes a convolution model and a cepstral filtering algorithm for reducing the blocking artifacts in compressed images and videos. Since each of the horizontal or vertical lines of the compressed image has abrupt changes at almost every 8th sample, images can be modeled as a convolution of the original sequence with an excitation signal of periodicity 8. Comparison of the cepstrums also shows that the multiples of 8th samples of the compressed signal's cepstrum usually have larger magnitudes than those of the original. The proposed filtering algorithm is to reduce the magnitudes of every 8th sample of the cepstrum, and disperse the reduced values to the neighboring samples. The algorithm is conducted in the cepstrum domain for reducing only the excitation signal which is the cause of blocking artifacts. Hence, the high frequency contents are not reduced by the postprocessing, whereas the conventional lowpass filtering blurs the image.

Weighted roll-off scheme to remove block artifacts for low power local dimming liquid crystal displays

FIR-SDE: fast image restoration via mean-reverting stochastic differential equation

An efficient approach toward a no-reference ringing metric intrinsically exists of two steps: first detecting regions in an image where ringing might occur, and second quantifying the ringing annoyance in these regions. This paper presents a novel approach toward the first step: the automatic detection of regions visually impaired by ringing artifacts in compressed images. It is a no-reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). To maintain low complexity for real-time applications, the proposed approach adopts a perceptually relevant edge detector to capture regions in the image susceptible to ringing, and a simple yet efficient model of visual masking to determine ringing visibility. The approach is validated with the results of a psychovisual experiment, and its performance is compared to existing alternatives in literature for ringing region detection. Experimental results show that our method is promising in terms of both reliability and computational efficiency.

In this paper, I observed the properties of HVS for Ring Detection. The automatic detection of regions visually impaired by ringing artifacts in compressed images, it is a no-reference approach, taking into account the specific physical structure of ringing artifacts combined with properties of the human visual system (HVS). The approach is validated with the results of a psycho visual experiment, and its performance is compared to existing alternatives in literature for ringing region detection. Experimental results show that our method is appropriate in terms of both reliability and computational efficiency.

Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration