A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints

Chen, Ke; Harding, Simon; Williams, Bryan; Zheng, Yalin

doi:10.1155/2015/860263

Cited by 7 publications

(8 citation statements)

References 49 publications

(86 reference statements)

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“…Chen et al 28 proposed an improved way of imposing such constraints by enforcing two of them implicitly in the functional, resulting in the minimisation of the functional…”

Section: Two-stage Restoration and Segmentation Of Images With Unknowmentioning

confidence: 99%

“…Using these and equation (33), we make the initial estimates of c 1 and c 2 . We then proceed to solve the model (28), alternately minimising with respect to the arguments. The final segmentation is then given by the contour À p derived from the final function .…”

Section: A Relaxed Model For the Segmentation Of Images With Unknown mentioning

confidence: 99%

“…Chen et al. 28 proposed an improved way of imposing such constraints by enforcing two of them implicitly in the functional, resulting in the minimisation of the functional where the functions R 1 and R 2 denote total variation regularisation for the image and blur function, respectively and scriptA denotes the ADMM term which is used to penalise the distance between the image (respectively blur) function and transformed ψ (respectively ω ) functions and which is given by …”

Section: Segmentation Of Images Corrupted By Unknown Blurmentioning

confidence: 99%

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An effective variational model for simultaneous reconstruction and segmentation of blurred images

Williams

Spencer

Chen

et al. 2016

Journal of Algorithms & Computational Technology

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The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

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“…Chen et al 28 proposed an improved way of imposing such constraints by enforcing two of them implicitly in the functional, resulting in the minimisation of the functional…”

Section: Two-stage Restoration and Segmentation Of Images With Unknowmentioning

confidence: 99%

Section: A Relaxed Model For the Segmentation Of Images With Unknown mentioning

confidence: 99%

Section: Segmentation Of Images Corrupted By Unknown Blurmentioning

confidence: 99%

See 1 more Smart Citation

An effective variational model for simultaneous reconstruction and segmentation of blurred images

Williams

Spencer

Chen

et al. 2016

Journal of Algorithms & Computational Technology

Self Cite

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“…This is typically done in a naive way which can lead to a significant drop in the quality of the recovered image [2]. To address this, we implement a model for implicitly constrained deblurring which is known to provide a better result [4].…”

Section: Image Deconvolution For Colour Fundus Imagingmentioning

confidence: 99%

A New Method of Blind Deconvolution for Colour Fundus Retinal Images

Williams

Chen

Harding

et al. 2015

Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop

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Abstract. Fundus retinal imaging is widely used in the diagnosis and management of eye disease. Blur commonly occurs in the acquisition and when it is severe the resulting loss of resolution hampers accurate clinical assessment. In this paper, we present a new technique to address this challenging problem. We make use of implicitly constrained image deblurring, which is known to provide improved results over unconstrained and explicitly constrained methods, and build this into a multi-channel variational framework for parametric deblurring. We propose a new method for automatically selecting the regularisation parameter in the absence of the true (sharp) image using vessel segmentation. We then modify the model to include a regularisation coefficient function which is dependent on an available image mask in order to avoid potential inaccuracies caused by the addition of artificial masks. We present experimental results to demonstrate the effectiveness of our new method.

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“…One approach to devise efficient SIC and PIC detectors is to exploit the communication system properties such as data sparseness, noise statistical information etc. The non-negativity information is exploited in many applications such medical and astronomical imaging [17][18][19], non-negative matrix and tensor factorisation [20,21], blind source separation [22], and adaptive filtering [23][24][25] etc just to name a few. It is extended recently to communication systems that possess this property.…”

Section: Introductionmentioning

confidence: 99%