Perceptually Optimized Coded Apertures for Defocus Deblurring

Masiá, Belén; Presa, Lara; Corrales, Adrian; Gutiérrez, Diego

doi:10.1111/j.1467-8659.2012.03067.x

Cited by 23 publications

(15 citation statements)

References 19 publications

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“…Image quality metrics have been successfully applied in a variety of applications, such as automatic parameter selection [Zhu and Milanfar 2010;Mittal et al 2012a], blur-aware and noise-aware downsizing [Samadani et al 2010;Trentacoste et al 2011], and coded aperture design [Masia et al 2012]. We will discuss the difference between these methods and our approach in more detail in Sec.…”

Section: Background and Related Workmentioning

confidence: 98%

“…Image quality metrics can be classified into two categories: full-reference and no-reference, depending on whether the ground truth image is needed. Commonly used fullreference metrics include PSNR [Teo and Heeger 1994], multiscale SSIM [Wang et al 2004], VIF [Sheikh and Bovik 2006], HDR-VDP-2 , and a linear combination of them [Masia et al 2012]. Although these metrics have been widely used for evaluating subtle image corruption, they are not able to measure the perceptual impact of the significant artifacts introduced by a variety of graphics and vision algorithms, such as image deblurring, as we will demonstrate later, and photo-realistic rendering, as demonstrated by Cadik et al [2012].…”

Section: Background and Related Workmentioning

confidence: 99%

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A no-reference metric for evaluating the quality of motion deblurring

et al. 2013

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Figure 1: We develop a no-reference metric for evaluating the perceptual quality of image motion deblurring results. The metric can be used for fusing multiple deblurring results (b) of the same input image (a) to generate one with the best quality (e). (c) and (d) are the result of simply averaging all deblurring results and the result of a naive fusion method, respectively. See text in Sec. 6.3 for more details. Original image courtesy digital cat@Flickr. AbstractMethods to undo the effects of motion blur are the subject of intense research, but evaluating and tuning these algorithms has traditionally required either user input or the availability of ground-truth images. We instead develop a metric for automatically predicting the perceptual quality of images produced by state-of-the-art deblurring algorithms. The metric is learned based on a massive user study, incorporates features that capture common deblurring artifacts, and does not require access to the original images (i.e., is "noreference"). We show that it better matches user-supplied rankings than previous approaches to measuring quality, and that in most cases it outperforms conventional full-reference image-similarity measures. We demonstrate applications of this metric to automatic selection of optimal algorithms and parameters, and to generation of fused images that combine multiple deblurring results.

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Section: Background and Related Workmentioning

confidence: 98%

Section: Background and Related Workmentioning

confidence: 99%

A no-reference metric for evaluating the quality of motion deblurring

et al. 2013

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“…Another interesting option would be to combine our approach with perceptual aspects taking into account the characteristics of the human visual system (e.g. [MMG11,MPCG12]).…”

Section: Discussionmentioning

confidence: 99%

Practical Low‐Cost Recovery of Spectral Power Distributions

Alvarez-Cortes

Kunkel

Masiá

2015

Computer Graphics Forum

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Measuring the spectral power distribution of a light source, that is, the emission as a function of wavelength, typically requires the use of spectrophotometers or multi-spectral cameras. Here, we propose a low-cost system that enables the recovery of the visible light spectral signature of different types of light sources without requiring highly complex or specialized equipment and using just off-the-shelf, widely available components. To do this, a standard Digital Single-Lens Reflex (DSLR) camera and a diffraction filter are used, sacrificing the spatial dimension for spectral resolution. We present here the image formation model and the calibration process necessary to recover the spectrum, including spectral calibration and amplitude recovery. We also assess the robustness of our method and perform a detailed analysis exploring the parameters influencing its accuracy. Further, we show applications of the system in image processing and rendering.

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“…These methods [6], [33], [34] can identify loss or distortions of salient regions in the original image, but again they do not take aesthetics into account. Last, used in the context of deblurring algorithms, Masia et al [35] propose an image quality measure which combines the L 2 norm, the SSIM index and the HDR-VDP-2 metric. The SSIM index performs well in measuring the structure similarity between two images, while the HDR-VDP-2 produces a good estimation on the overall quality of an image; since both of these aspects are important in image retargeting, we employ these two metrics as well when measuring structure preservation.…”

Section: Related Workmentioning

confidence: 99%

Objective Quality Prediction of Image Retargeting Algorithms

Liang

Liu

Gutiérrez

2017

IEEE Trans. Visual. Comput. Graphics

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Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference.

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Perceptually Optimized Coded Apertures for Defocus Deblurring

Cited by 23 publications

References 19 publications

A no-reference metric for evaluating the quality of motion deblurring

A no-reference metric for evaluating the quality of motion deblurring

Practical Low‐Cost Recovery of Spectral Power Distributions

Objective Quality Prediction of Image Retargeting Algorithms

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