Application of detector precision characteristics for the denoising of biological micrographs in the wavelet domain

Bernaś, Tytus; Starosolski, Roman; Wójcicki, Robert

doi:10.1016/j.bspc.2015.02.010

Cited by 10 publications

(5 citation statements)

References 15 publications

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“…Histograms of these images may be globally or locally highly sparse [28,29] and A3-red.3x are improved by 4.6% to 6.9%. By using more advanced denoising filters, we would probably obtain better results for a broader range of images, in particular when the detector characteristics are known or may be determined beforehand (e.g., for a specific camera's RAW files), since noise parameters and proper denoising filter parameters may in such cases be estimated directly from the acquisition process parameters (e.g., see [14,30]).…”

Section: Effects Of Rdls On Bitrates Of Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…An unwanted side effect of established color space transformation, like RCT, is that while removing correlation it contaminates all transformed components with noise from other components. Components of images acquired by consumer acquisition devices operating at high photon flux are mainly affected by additive white Gaussian noise [14]; however, noise parameters may differ among components due to differences among primary color detector cells or detector filters. The luminance component of RCT contains a fraction of the noise from all the primary color components and each chrominance component contains noise from two primary colors.…”

Section: Introductionmentioning

confidence: 99%

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Reversible denoising and lifting based color component transformation for lossless image compression

Starosolski

2019

Multimed Tools Appl

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An undesirable side effect of reversible color space transformation, which consists of lifting steps, is that while removing correlation it contaminates transformed components with noise from other components. To remove correlation without increasing noise, we integrate denoising into the lifting steps and obtain a reversible image component transformation. For JPEG-LS, JPEG 2000, and JPEG XR algorithms in lossless mode, we find that the proposed method applied to the RDgDb color space transformation with a simple denoising filter is especially effective for images in the native optical resolutions of acquisition devices, but may lead to increased bitrates for typical images. We also present an efficient estimator of image component transformation effects.

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Section: Effects Of Rdls On Bitrates Of Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reversible denoising and lifting based color component transformation for lossless image compression

Starosolski

2019

Multimed Tools Appl

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“…A promising direction of further research is the use of the detector precision characteristic (DPC) method [ 39 ], which allows for a virtually costless adaptive construction of the transform based on a model that is driven by image acquisition parameters, which are normally stored along with medical volumes. We have already obtained positive results by employing DPC to adaptively select denoising filters for RDLS-modified color space transforms [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Starosolski

2020

Entropy

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The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.

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“…Interestingly, there are two approaches that might allow avoiding the use of the heuristic and transmitting the selected filters. In the case of images, whose acquisition parameters are known and should be stored along with the compressed image, the detector precision characteristic (DPC) approach [ 31 ] may be applied; it constructs the acquisition device model that allows for immediate selection of the denoising filters for the image based directly on the acquisition process parameters. The so-called single image noise level estimation or single image denoising algorithms [ 32 , 33 ] find parameters of a denoising filter for an image based on analysis of this image only and independently of the further processing of this image.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

Starosolski¹

2020

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A new hybrid transform for lossless image compression exploiting a discrete wavelet transform (DWT) and prediction is the main new contribution of this paper. Simple prediction is generally considered ineffective in conjunction with DWT but we applied it to subbands of DWT modified using reversible denoising and lifting steps (RDLSs) with step skipping. The new transform was constructed in an image-adaptive way using heuristics and entropy estimation. For a large and diverse test set consisting of 499 photographic and 247 non-photographic (screen content) images, we found that RDLS with step skipping allowed effectively combining DWT with prediction. Using prediction, we nearly doubled the JPEG 2000 compression ratio improvements that could be obtained using RDLS with step skipping. Because for some images it might be better to apply prediction instead of DWT, we proposed compression schemes with various tradeoffs, which are practical contributions of this study. Compared with unmodified JPEG 2000, one scheme improved the compression ratios of photographic and non-photographic images, on average, by 1.2% and 30.9%, respectively, at the cost of increasing the compression time by 2% and introducing only minimal modifications to JPEG 2000. Greater ratio improvements, exceeding 2% and 32%, respectively, are attainable at a greater cost.

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Application of detector precision characteristics for the denoising of biological micrographs in the wavelet domain

Cited by 10 publications

References 15 publications

Reversible denoising and lifting based color component transformation for lossless image compression

Reversible denoising and lifting based color component transformation for lossless image compression

Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform

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