![signal to noise ratio signal to noise ratio](https://soundcertified.com/wp-content/uploads/2018/06/signal-to-noise-ratio-diagram.png)
The resonance frequency depends on the direct chemical environment (chemical shift). The signal from a fluorinated molecule is dependent on its resonance with the specific excitation frequency and receiving bandwidth. Assuming that noise sources are independent, the total SNR for aspecific illumination level (or dose) will be calculated by S N R = V s i g ∑ i N V n 2 where V 1, V 2 … V N are noise sources which can be signal dependent like the shot noise or signal independent like the analog chain noise floor. For low dose, the shot noise will become smaller than the noisefloor of the pixel and the analog chain. So the largest possible SNR of a perfect indirect sensor is the square root of the number of the X-ray photons. The scintillator gain by itself is not a deterministic process and thus, adds even more noise which is independent of the CIS being used. It is most important to note that in the case of an indirect sensor where one has large gain from X-ray to visible photons (at least several hundred) coming from the scintillator, the SNR will be determined by the X-ray photon shot noise. The RMS noise of the photon number equals the square root of the average dose hitting the pixel. The signal itself has Poissonic noise distribution known as ‘shot noise’. SNR of ~3 is considered ‘visible,’ namely an image can be seen on the background of the noise if the image signal level is at least about 3x higher than the noise level. This of course depends on the object, the illumination (or dose) and the sensor quality, and thus it cannot serve as a figure of merit of a sensor. SNR is the one parameter which quantizes our ability to resolve the required image from the noise it is embedded in. Fenigstein, in High Performance Silicon Imaging, 2014 12.6.1 SNR The intrinsic noise can be reduced by increasing number of carriers in semiconductors (reducing resistance) and increasing the volume of thermal sensing element.Ī. Intrinsic sources of noise include Johnson noise (random motion of carrier) and 1/f noise ( Dinh et al., 2017a). While external and conducted noises can be removed and reduced by circuit designs and appropriate setup, intrinsic noise is unavoidable and can be reduced by appropriate design of heater and thermal sensing elements. There are different sources of noise, including external (electromagnetic), conducted and intrinsic noise ( Dao et al., 2004). Reduction of noise is used to increase the SNR. Thermal sensors with a high SNR are desired for a wide range of application. SNR refers to the ratio between the power of the desired output signal and the background noise, which is described as SNR dB = 2 log 10 V signal V noise, where V signal and V noise are the measured signal voltage and noise voltage, respectively. Nam-Trung Nguyen, in Reference Module in Biomedical Sciences, 2021 High signal-to-noise ratio (SNR) For smaller images with the size 256×256, PWT performance is better than others at all the bit rates. For CT real-time images and for 512×512 images, PWT showed better performance at higher bit rates. In case of SSIM, the larger images with size 1024×1024 showed better performance using PCT and PRT at all bit rates compared to PWT. PSNR compares the reference image with the reconstructed image and does numerical comparison, but SSIM takes into account the biological factors of the Human Visual System (HVS) and calculates the structural similarity between them. For 1024×1024 sized CT online images, PCT and PRT gave good results compared to PWT at all bit rates. The table shows the BPP at which best PSNR is obtained for all the test images. For other modalities, PCT and PRT perform better at lower bit rates and PWT gave good PSNR at higher bit rates. In terms of PSNR, the PWT-based compression approach performs better for CT real-time images for all sizes and for all bit rates. SSIM using test images of different sizes and modalities.