The computing power in modern digital imaging devices allows complex denoising algorithms. The negative in uence of denoising on the reproduction of low contrast, ne details is also known as texture loss. Using the dead leaves structure is a common technique to describe the texture loss which is currently discussed as a standard method in workgroups of ISO and CPIQ. We present our experience using this method. Based on real camera data of several devices, we can point out where weak points in the SFRDeadLeaves method are and why results should be interpreted carefully. The SFRDeadLeaves approach follows the concept of a semi-reference method, so statistical characteristics of the target are compared to statistical characteristics in the image. In the case of SFRDeadLeaves, the compared characteristic is the power spectrum. The biggest disadvantage of using the power spectrum is that phase information is ignored, as only the complex modulus is used. We present a new approach, our experience with it and compare it to the SFR Dead Leaves method. The new method follows the concept of a full-reference method, which is an intrinsic comparison of image data to reference data.
KEYWORDS: Image compression, Signal to noise ratio, Quantization, Interference (communication), Image quality, Data modeling, Computer programming, Cameras, Sensors, Data compression
The study investigates the lossy compression of DSC raw data based upon the 12 bit baseline JPEG compression.
Computational simulations disclose that JPEG artefacts originate from the quantization of the DCT coefficients. Input noise is shown to serve as an appropriate means to avoid these artefacts. Stimulated by such a noise, the JPEG encoder simply acts as an high frequency noise generator.
The processing structure of a general compression model is introduced. The four color planes of an image sensor are separately compressed by a 12 bit baseline JPEG encoder. One-dimensional look-up-tables allow for an optimized adaptation of the JPEG encoder to the noise characteristics of the input signals. An idealized camera model is presumed to be dominated by photon noise. Its noise characteristics can optimally be matched to the JPEG encoder by a common gamma function.
The gamma adapted compression model is applied to an exemplary set of six raw images. Its performance concerning the compression ratio and compression noise is examined.
Optimally adjusted to the input noise, the compression procedure offers excellent image quality without any perceived loss referring to sharpness or noise. The results show that this method is capable to achieve compression ratios of about factor 4 in practice. The PSNR reaches about 60 dB over the complete signal range.
We present a novel method for intra-frame image processing, which is applicable to a wide variety of medical imaging modalities, like X-ray angiography, X-ray fluoroscopy, magnetic resonance, or ultrasound. The method allows to reduce noise significantly - by about 4.5 dB and more - while preserving sharp image details. Moreover, selective amplification of image details is possible. The algorithm is based on a multi-resolution approach. Noise reduction is achieved by non-linear adaptive filtering of the individual band pass layers of the multi-resolution pyramid. The adaptivity is controlled by image gradients calculated from the next coarser layer of the multi-resolution pyramid representation, thus exploiting cross-scale dependencies. At sites with strong gradients, filtering is performed only perpendicular to the gradient, i.e. along edges or lines. The multi-resolution approach processes each detail on its appropriate scale so that also for low frequency noise small filter kernels are applied, thus limiting computational costs and allowing a real-time implementation on standard hardware. In addition, gradient norms are used to distinguish smoothly between “structure” and “noise only” areas, and to perform additional noise reduction and edge enhancement by selectively attenuating or amplifying the corresponding band pass coefficients.
In continuous X-ray fluoroscopy images are sometimes blurred uniformly due to motion of the operating table. Additionally, low-dose fluoroscopy images are degraded by relatively strong quantum noise, which is not affected by the blur. We quantify the degradation due to motion blur by assessing the blur's effect on the Detective Quantum Efficiency (DQE), which captures the signal- and noise transfer properties of an imaging system. The estimation of the motion blur parameters, viz. direction and extent, is carried out one after the other. The central idea for direction detection is to apply an inertia-like matrix to the global spectrum of the degraded image, which assesses the anisotropy caused by the blur. Once the blur direction is obtained by this tensor approach, its extent is identified from an estimated power spectrum or bispectrum slice along this direction. The decision for either method is based on the eigenvalues of the inertia matrix. The blur parameters are used as input for a nonlinear Maximum-a- posteriori restoration technique based on a Generalized Gauss- Markov Random field for which several efficient optimization strategies are presented. This approach includes a thresholdless edge model. The DQE is generalized as a quality measure to assess the signal- and noise transfer properties of the restoration method.
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