Locally adaptive image filtering based on learning with clustering

Ponomarenko, Nikolay N.; Zelensky, Alexander A.; Егиазарян, Карен; Astola, Jaakko

doi:10.1117/12.583222

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The proposed algorithm uses three common image databases to evaluate the performance: the LIVE2 database [10], the CSIQ database [11] and the TID2013 database [12]. After testing the HFSVD algorithm, the scatter plots of the subjective value versus objective value are shown in Figure 3.…”

Section: Experimental Analysis and Discussionmentioning

confidence: 99%

A No-reference Blur Image Quality Assessment Algorithm Based on Wavelet Singular Value Decomposition

Huang¹,

Si-si²,

Cao³

et al. 2017

dtetr

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Abstract.A simple and effective no reference blur image quality assessment algorithm based on wavelet high frequency singular value decomposition is proposed. As the different wavelet high frequency sub-bands in the same level are highly structural correlation, and the degree of correlation would be weaken as the degree of blur distortion strengthen. The proposed method decomposes the images by wavelet transform firstly. Then the singular value decomposition is used for different high frequency sub-bands to get their singular value vectors, which we used to represent their structural information. Thirdly the angles between different sub-bands singular value vectors are computed, which reflects their degree of correlation. Finally the sum of angles is used as the last objective assessment index. Compared to the traditional methods, the proposed algorithm is more efficient and practical as it does not need to train or create a reference image. Experimental results show its good effectiveness and performance on LIVE2, CSIQ and TID2013 databases. IntroductionNowadays digital images are widely used in various applications. But how to assess the quality of images objectively and effectively still remains a challenge [1]. Based on the availability of reference images, with which the distorted image is to be compared, objective image quality assessment (IQA) approaches can be classified into: full-reference (FR), no-reference (NR) and reduced-reference (RR) approaches. As the reference image is not available, no-reference approach is more widely used and has been the research hot spot in image quality assessment [2].The proposed method is a NR IQA method assumed to a blurry image. In this field, Rony Ferzli, etc[3] presented a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Xie Xiaofu, etc [4] introduced an no-reference structural sharpness(NRSS) method for quality evaluation of blurred images by constructing a reference image by a low-pass filter. Considering the human visual system, Yin Ying, etc [5] compute the blur value of image by producing a reference image through Gauss low-pass filter transformation and combining the singular value decomposition(SVD) and generalized regression neural network model techniques. These methods can be roughly reduced into two categories. The first category methods construct a reference image through some sort of filtering process and then evaluates the quality by comparing the difference between the before and after filtered image, such as in literature [4,5]. The second category methods firstly make some transformation to the image to get the feature information, and then predict image quality score by some kinds of machine learning models. The above two category methods needs either to construct a reference image or to train and learn with the opinion quality score by machine learning to predict the image quality, these will undoubtedly increase the computing cost and a...

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Section: Experimental Analysis and Discussionmentioning

confidence: 99%

A No-reference Blur Image Quality Assessment Algorithm Based on Wavelet Singular Value Decomposition

Huang¹,

Si-si²,

Cao³

et al. 2017

dtetr

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“…From the viewpoint of noise suppression, preservation of edges, details and texture, and time efficiency requirements, quite good effectiveness has been demonstrated by locally adaptive methods [15][16][17]. The latest modifications of locally adaptive filters [16,17] include both typical nonlinear scanning window filters (employing order statistics) and transform-based filters, in particular, filters based on discrete cosine transform (DCT).…”

Section: Introductionmentioning

confidence: 99%

Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

Öktem

Егиазарян

Lukin

et al. 2007

EURASIP J. Adv. Signal Process.

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This work addresses the problem of signal-dependent noise removal in images. An adaptive nonlinear filtering approach in the orthogonal transform domain is proposed and analyzed for several typical noise environments in the DCT domain. Being applied locally, that is, within a window of small support, DCT is expected to approximate the Karhunen-Loeve decorrelating transform, which enables effective suppression of noise components. The detail preservation ability of the filter allowing not to destroy any useful content in images is especially emphasized and considered. A local adaptive DCT filtering for the two cases, when signaldependent noise can be and cannot be mapped into additive uncorrelated noise with homomorphic transform, is formulated. Although the main issue is signal-dependent and pure multiplicative noise, the proposed filtering approach is also found to be competing with the state-of-the-art methods on pure additive noise corrupted images.

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Automatic Design of Locally Adaptive Filters for Pre-processing of Images Subject to Further Interpretation

Ponomarenko¹,

Zelensky²,

Astola

et al.

2006 IEEE Southwest Symposium on Image Analysis and Interpretation

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Locally adaptive filters are widely used in image processing applications. However, their design commonly requires sufficient efforts and does not take into consideration some important aspects of further processing (interpreting and/or classification) of images. This paper puts forward a novel approach to automatic design of locally adaptive filters subject to further interpretation, namely, detection and localization of small size objects. Design is based on learning with clustering for a test image corrupted by a noise with statistical characteristics observed in real life images to which the obtained filter intend to be further applied. Quantitative data confirming the designed filter efficiency are presented.

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Locally adaptive image filtering based on learning with clustering

Cited by 6 publications

References 20 publications

A No-reference Blur Image Quality Assessment Algorithm Based on Wavelet Singular Value Decomposition

A No-reference Blur Image Quality Assessment Algorithm Based on Wavelet Singular Value Decomposition

Locally Adaptive DCT Filtering for Signal-Dependent Noise Removal

Automatic Design of Locally Adaptive Filters for Pre-processing of Images Subject to Further Interpretation

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