Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

Bosco, A.; Battiato, Sebastiano; Bruna, Arcangelo; Rizzo, Rosetta

doi:10.3390/s90301692

Cited by 19 publications

(19 citation statements)

References 33 publications

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“…The single frame denoising method [31] exploits characteristics of the human visual system and sensor noise statistics. In the demosaicing method [15], the LMMSE filtering for considering spatial and spectral correlations was utilized to reconstruct a full color image.…”

Section: Resultsmentioning

confidence: 99%

“…As illustrated in Figure 10 and Figure 11, the visual results of the proposed method are better than those of the other methods. Although the method from [31] effectively removes noise and preserves edges or textures, color artifacts appear, especially in the characters, as shown in Figure 10c. The remaining noise appears in the results of Figure 10c and Figure 11c compared with the proposed method.…”

Section: Resultsmentioning

confidence: 99%

“…To address these disadvantages, some denoising methods have been directly used with the CFA [30,31,32], as shown in Figure 1c. In the proposed approach, a denoising algorithm is also performed in the CFA pattern before the demosaicing process, as shown in Figure 1c.…”

Section: Introductionmentioning

confidence: 99%

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Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation

Lee

Kang

2017

Sensors

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In this paper, a spatio-spectral-temporal filter considering an inter-channel correlation is proposed for the denoising of a color filter array (CFA) sequence acquired by CCD/CMOS image sensors. Owing to the alternating under-sampled grid of the CFA pattern, the inter-channel correlation must be considered in the direct denoising process. The proposed filter is applied in the spatial, spectral, and temporal domain, considering the spatio-tempo-spectral correlation. First, nonlocal means (NLM) spatial filtering with patch-based difference (PBD) refinement is performed by considering both the intra-channel correlation and inter-channel correlation to overcome the spatial resolution degradation occurring with the alternating under-sampled pattern. Second, a motion-compensated temporal filter that employs inter-channel correlated motion estimation and compensation is proposed to remove the noise in the temporal domain. Then, a motion adaptive detection value controls the ratio of the spatial filter and the temporal filter. The denoised CFA sequence can thus be obtained without motion artifacts. Experimental results for both simulated and real CFA sequences are presented with visual and numerical comparisons to several state-of-the-art denoising methods combined with a demosaicing method. Experimental results confirmed that the proposed frameworks outperformed the other techniques in terms of the objective criteria and subjective visual perception in CFA sequences.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation

Lee

Kang

2017

Sensors

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“…Tone management is achieved in [1], also using the bilateral filter. Other studies have been carried out for other sensors, such as CMOS [2], but for the best of our knowledge none of them uses a RGB-D sensor. Other advanced color smoothing methods, like BM3D [13] or wavelet have been reported.…”

Section: Related Workmentioning

confidence: 99%

Color smoothing for RGB-D data using entropy information

Navarrete

Viejo

Cazorla

2016

Applied Soft Computing

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RGB-D sensors are capable of providing 3D points (depth) together with color information associated with each point. These sensors suffer from different sources of noise. With some kinds of RGB-D sensors, it is possible to preprocess the color image before assigning the color information to the 3D data.However, with other kinds of sensors that is not possible: RGB-D data must be processed directly. In this paper, we compare different approaches for noise and artifacts reduction: Gaussian, mean and bilateral filter. These methods are time consuming when managing 3D data, which can be a problem with several real time applications. We propose new methods to accelerate the whole process and improve the quality of the color information using entropy information.Entropy provides a framework for speeding up the involved methods allowing certain data not to be processed if the entropy value of that data is over or under a given threshold. The experimental results provide a way to balance the quality and the acceleration of these methods. The current results show that our methods improve both the image quality and processing time, as compared to the original methods.

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“…[1][2][3] Furthermore, mobile imaging systems are becoming more and more sophisticated with the introduction of features that were before relegated to high-end devices only. Features such as the availability of raw image data for offline processing are being introduced in the market too, hence noise profiling of image sensors maintains its importance for obtaining the best image quality from the imaging pipeline.…”

Section: Introductionmentioning

confidence: 99%

Image sensor noise profiling by voting based curve fitting

et al. 2014

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The output quality of an image filter for reducing noise without damaging the underlying signal, strongly depends on the accuracy of the noise model in characterizing the noise introduced by the acquisition device. In this paper we provide a solution for characterizing signal dependent noise injected at shot time by the image sensor. Different fitting models describing the behavior of noise samples are analyzed, with the aim of finding a model that offers the most accurate coverage of the sensor noise under any of its operating conditions. The noise fitting equation minimizing the residual error is then identified. Moreover, a novel algorithm able to obtain the noise profile of a generic image sensor without the need of a controlled environment is proposed. Starting from a set of heterogeneous CFA images, by using a voting based estimator, the parameters of the noise model are estimated.

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Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

Cited by 19 publications

References 33 publications

Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation

Denoising Algorithm for CFA Image Sensors Considering Inter-Channel Correlation

Color smoothing for RGB-D data using entropy information

Image sensor noise profiling by voting based curve fitting

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