Multichannel image deblurring of raw color components

Trimeche, Mejdi; Paliy, Dmitry; Vehviläinen, Markku; Katkovnic, Vladimir

doi:10.1117/12.586598

Cited by 23 publications

(15 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the computed PSF is not space-resolved but applies for the whole image. The same holds for the work of Trimeche et al, 5 where a checkerboard pattern is used as a template for PSF estimation. Joshi et al 4 account for the spatial dependency of the PSF and utilize a test chart with a circular checkerboard pattern.…”

Section: Introductionmentioning

confidence: 82%

See 1 more Smart Citation

Direct PSF estimation using a random noise target

2010

View full text Add to dashboard Cite

Conventional point spread function (PSF) measurement methods often use parametric models for the estimation of the PSF. This limits the shape of the PSF to a specific form provided by the model. However, there are unconventional imaging systems like multispectral cameras with optical bandpass filters, which produce an, e.g., unsymmetric PSF. To estimate such PSFs we have developed a new measurement method utilizing a random noise test target with markers: After acquisition of this target, a synthetic prototype of the test target is geometrically transformed to match the acquired image with respect to its geometric alignment. This allows us to estimate the PSF by direct comparison between prototype and image. The noise target allows us to evaluate all frequencies due to the approximately "white" spectrum of the test target -we are not limited to a specifically shaped PSF. The registration of the prototype pattern gives us the opportunity to take the specific spectrum into account and not just a "white" spectrum, which might be a weak assumption in small image regions. Based on the PSF measurement, we perform a deconvolution. We present comprehensive results for the PSF estimation using our multispectral camera and provide deconvolution results.

show abstract

Section: Introductionmentioning

confidence: 82%

“…4c is usually utilized in geometric camera calibration tasks. Trimeche et al 5 use it as a template for the PSF measurement. However, its frequency spectrum shown in Fig.…”

Section: Physical Backgroundmentioning

confidence: 99%

Direct PSF estimation using a random noise target

2010

View full text Add to dashboard Cite

show abstract

“…We therefore decide against both natural scenes and edge-based patterns (e.g., Trimeche et al [2005] and Joshi et al [2008]), and instead adopt a white noise pattern. See Brauers et al [2010] for a comparison of a white noise pattern to different other calibration patterns.…”

Section: Calibration Patternmentioning

confidence: 99%

High-quality computational imaging through simple lenses

et al. 2013

View full text Add to dashboard Cite

Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing.In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.

show abstract

“…The main drawback of applying demosaicking and deconvolution sequently is that input noise for deconvolution becomes correlated, which inherently needs more complex denoising algorithms. Recently, joint demosaicking and deconvolution have been studied by various researchers [3,4,5,6]. In [3], Har-Noy et al combine a deconvolved luminance channel with a fast demosaicked blurred RGB to remove motion blur.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], Har-Noy et al combine a deconvolved luminance channel with a fast demosaicked blurred RGB to remove motion blur. Paliy et al focused on removing Poisson noise using LPA-ICI [4], the same LPA-ICI approach is applied in [6] as a preprocessing step. Soulez and Thiébaut developed a Bayesian restoration method using edge-preserving spatial and spectral regularization [5].…”

Section: Introductionmentioning

confidence: 99%

A primal-dual algorithm for joint demosaicking and deconvolution

Luong

Goossens

Aelterman

et al. 2012

2012 19th IEEE International Conference on Image Processing

View full text Add to dashboard Cite

In this paper, we present a first-order primal-dual algorithm for tackling the joint demosaicking and deconvolution problem. The proposed algorithm exploits the sparsity of both discrete gradient (TV) and shearlet coefficients as prior knowledge. In order to deal with this sparsity across the color channels, we first decorrelate the signals in color space before sparsifying them spatially, resulting in a separable transform. We demonstrate that this approach yields better results than employing group sparsity strategies. We propose to update the decorrelation operator during the image reconstruction, this approach will result in a significant improvement in PSNR. By relaxing the sparsity of the chrominance signals, we obtain both better objective and subjective image quality compared to other stateof-the-art demosaicking and deconvolution algorithms. Also, color artifacts due to demosaicking are suppressed very well.

show abstract

Multichannel image deblurring of raw color components

Cited by 23 publications

References 16 publications

Direct PSF estimation using a random noise target

Direct PSF estimation using a random noise target

High-quality computational imaging through simple lenses

A primal-dual algorithm for joint demosaicking and deconvolution

Contact Info

Product

Resources

About