Real-time bayer-domain image restoration for an extended depth of field (EDoF) camera

Kim, Sangjin; Jun, Sinyoung; Lee, Eunsung; Shin, Jeongho; Paik, Joonki

doi:10.1109/tce.2009.5373728

Cited by 20 publications

(19 citation statements)

References 23 publications

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“…(24). Considering that the size of the filter H −1 is approximately equivalent to the size of the blurred image in the spatial-domain filter, a practical truncation method 30 is used to realize an FIR filtering structure. Because the architecture is a pipeline, the restoration can be executed in real time but with a little delay.…”

Section: Architecture Solution For Real Timementioning

confidence: 99%

Fast approach to infrared image restoration based on shrinkage functions calibration

Zhang

Shi

et al. 2016

Opt. Eng

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, "Fast approach to infrared image restoration based on shrinkage functions calibration," Opt. Eng. 55(5), 053107 (2016), doi: 10.1117/1.OE.55.5.053107. Abstract. High-quality image restoration in real time is a challenge for infrared imaging systems. We present a fast approach to infrared image restoration based on shrinkage functions calibration. Rather than directly modeling the prior of sharp images to obtain the shrinkage functions, we calibrate them for restoration directly by using the acquirable sharp and blurred image pairs from the same infrared imaging system. The calibration method is employed to minimize the sum of squared errors between sharp images and restored images from the blurred images. Our restoration algorithm is noniterative and its shrinkage functions are stored in the look-up tables, so an architecture solution of pipeline structure can work in real time. We demonstrate the effectiveness of our approach by testing its quantitative performance from simulation experiments and its qualitative performance from a developed wavefront coding infrared imaging system.

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Section: Architecture Solution For Real Timementioning

confidence: 99%

Fast approach to infrared image restoration based on shrinkage functions calibration

Zhang

Shi

et al. 2016

Opt. Eng

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“…Kim et al has proposed the original version of the TCLS restoration filter for removing spatially adaptive image degradation followed by a spatially adaptive noise smoothing filter based on local variance [6]. This section presents a directionally adaptive version of the TCLS restoration filter that can minimize image degradation caused by weighted linear and bi-cubic interpolation.…”

Section: Directionally Adaptive Image Restorationmentioning

confidence: 99%

“…As a result, the proposed interpolation method can provide digitally interpolated images without interpolation artifacts such as jagged edges. Image details lost in the interpolation process are also recovered using the directionally adaptive truncated constrained least-squares (TCLS) filter [6].…”

Section: Introductionmentioning

confidence: 99%

Real-time super-resolution for digital zooming using finite kernel-based edge orientation estimation and truncated image restoration

Kang

Jeon

Lee

et al. 2013

2013 IEEE International Conference on Image Processing

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This paper presents a novel real-time super-resolution (SR) method using directionally adaptive image interpolation and image restoration. The proposed interpolation method estimates the edge orientation using steerable filters and performs edge refinement along the estimated edge orientation. Bi-linear and bi-cubic interpolation filters are then selectively used according to the estimated edge orientation for reducing jagging artifacts in slanting edge regions. The proposed restoration method can effectively remove image degradation caused by interpolation using the directionally adaptive truncated constrained least-squares (TCLS) filter. The proposed method provides high-quality magnified images which are similar to or better than the result of advanced interpolation or SR methods without high computational load. Experimental results indicate that the proposed system gives higher peak-to-peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values than the state-of-the-art image interpolation methods.

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“…For removing image degradation described in the previous subsection, we use the truncated constrained least squares (TCLS) filter, which can be implemented in the form of a finite impulse response (FIR) filter and enables spacevariant filtering [3] [4]. The frequency response of the original constrained least squares (CLS) filter is given as […”

Section: B Image Enhancement On Peripheral Regionmentioning

confidence: 99%

Peripheral region enhancement of geometrically compensated images from a fisheye lens

Kim

Paik

et al. 2011

2011 IEEE International Conference on Consumer Electronics -Berlin (ICCE-Berlin)

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In this paper, we propose a peripheral region enhancement method for geometrically compensated images from a fisheye lens. The proposed method provides a novel filtering method based on the degradation model of a fisheye lens. More specifically, we first present a space-variant image degradation model using generic Gaussian function with geometric distortion. We then apply the truncated constraint least squares (TCLS) filter for restoring the peripheral region of the image in the real-time, space-adaptive manner. Experimental results demonstrate that the proposed method can enhance the peripheral region while keeping the highquality center region.

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Real-time bayer-domain image restoration for an extended depth of field (EDoF) camera

Cited by 20 publications

References 23 publications

Fast approach to infrared image restoration based on shrinkage functions calibration

Fast approach to infrared image restoration based on shrinkage functions calibration

Real-time super-resolution for digital zooming using finite kernel-based edge orientation estimation and truncated image restoration

Peripheral region enhancement of geometrically compensated images from a fisheye lens

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