The CARE network was trained using the Google Colaboratory Pro platform. The high-SNR ground truth images were generated by registering 30 low-SNR input images obtained from the same ROI and summing them.
#DENOISER 2 SPEED SKIN#
Low-SNR input images were acquired from human skin in vivo at the imaging speed of 180 frames/second. Study Design/Materials and Methods: Content-aware image restoration (CARE) network was trained with pairs of low-SNR input and high-SNR ground truth PCM images obtained from 309 distinctive regions of interest (ROIs). In this paper, we evaluated deep learning (DL)-based approach for reducing noise in PCM images acquired with a short exposure time. When PCM images are acquired with a short exposure time to reduce motion blur and enable real-time 3D imaging, the signal-to-noise ratio (SNR) is decreased significantly, which poses challenges in reliably analyzing cellular features.
#DENOISER 2 SPEED PORTABLE#
The DL-based denoising method needs to be further trained and tested for PCM images obtained from disease-suspicious skin lesions.Ībstract = "Background and Objective: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. Conclusions: Results showed the potential of using a DL-based method for denoising PCM images obtained at a high imaging speed. Qualitative image assessment by three confocal readers showed that CARE denoised images exhibited negligible noise more often than input images and non-DL filtered images. CARE denoising provided quantitatively and qualitatively better noise reduction than non-DL filtering methods. Banding noise, prominent in input images, was significantly reduced in CARE denoised images. Results: CARE denoising improved the image quality significantly, increasing similarity with the ground truth image by 1.9 times, reducing noise by 2.35 times, and increasing SNR by 7.4 dB. The denoising performance of the trained CARE network was quantitatively and qualitatively evaluated by using image pairs from 45 unseen ROIs. Blending fixes this as well.ĭenoiser occasionally removes too much detail on some fine geometry or textures, blending just a little bit of original render back onto the denoised result usually reintroduces enough of high frequency detail to make the area believable.īlending in the original noisy render often helps to cover up slight splotchiness/smudges caused by denoising.Background and Objective: Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. Blending fixes thisĭue to the threshold-based nature of denoiser, at certain places, it often generates small pools of noise in the areas under the threshold, which neighbour with completely denoised, super clean areas. Cameras usually capture images with at least a little bit of noise, so super clean images just look uncanny. Having an ability to blend denoised result with undenoised one has several benefits:ĭenoiser produces ultra clean surfaces, which often look to uncanny and unrealistic. If you choose to denoise your render, you have either an option to blend the denoiser result with the undenoised one, or you have an option to store denoised result in a separate VFB buffer, and do the blending yourself in post. In other renderers, denoising has one very powerful feature - it is not destructive. The denoising in Cycles works pretty great but the way it’s implemented really limits its usability.