Smartphone camera is becoming the primary choice for photography among general users due to its convenience and rapidly improving image quality. However, it is more prone to noise compared to a professional DSLR camera due to a smaller sensor. Image noise, especially in low-light situations, is a critical problem that must be addressed to obtain high quality photos. Image denoising has thus remained an important low level vision topic over years with both traditional and learning based techniques used for mitigating this problem. We propose an adaptive Deep Neural Network based Noise Reduction (DNN-NR) algorithm to address the denoising problem in smartphone images. Image noise was modeled from photos captured under different light settings using a Poisson-Gaussian noise model which better approximates the signaldependence (photon sensing) and stationary disturbances in the sensor data. Using this noise model, synthetic noisy datasets were prepared to mimic photos captured under varying light conditions and train the network. A noise correction map based on camera and image information like ISO, vignetting map and image gray level was provided as an input to the network. This correction map provides an indication of the local noise level to help the network adaptively denoise photos. Experimental results show that our adaptive neural network based denoising approach produced a significantly better denoised image with higher PSNR and MOS quality scores in comparison to a standard denoising method like CBM3D across varying light conditions. In addition, using a locally varying noise map helped in preserving more detail in denoised images.
The most recent High Dynamic Range (HDR) standard, HDR10+, achieves good picture quality by incorporating dynamic metadata that carry frame-by-frame information for tone mapping while most HDR standards use static tone mapping curves that apply across the entire video. Since it is laborious to acquire hand-crafted best-fitting tone mapping curve for each frame, there have been attempts to derive the curves from input images. This paper proposes the neural network framework that generates tone mapping on a frame-by-frame basis. Although a number of successful tone mapping operators (TMOs) have been proposed over the years, evaluation of tone mapped images still remains a challenging topic. We define an objective measure to evaluate tone mapping based on Non-Reference Image Quality Assessment (NR-IQA). Experiments show that the framework produces good tone mapping curves and makes the video more vivid and colorful.
KEYWORDS: Nanoparticles, Synthetic aperture radar, Carbon, Magnetic resonance imaging, Temperature metrology, Tissues, Dielectrics, Carbon nanotubes, Absorption, In vivo imaging
Specific absorption rate (SAR) heating using radiofrequency (RF) waves is affected by the RF frequency and amplitude,
and the conductivity of the tissue. Recently, conductive nanoparticles were demonstrated to induce hyperthermia in vitro
and in vivo upon irradiation with an external 13.56 MHz RF field. The addition of conductive nanoparticles was assumed
to increase the tissue conductivity and SAR. However, no quantitative studies have been performed that characterize the
conductivities of biocompatible colloids or tissues containing nanoparticles, and relate the conductivity to SAR.
The complex permittivities were measured for colloids containing single-wall carbon nanotubes (SWCNTs) in normal
saline with 0.32% w/v Pluronic F108 nonionic surfactant. The carbon concentrations of the colloids ranged from 0 to 88
mM. The permittivities were measured using a dielectric probe and RF network analyzer for RF frequencies from 200
MHz to 3 GHz. The nonionic surfactant was added to the colloids to minimize flocculation of the nanotubes during the
RF heating experiments. The results were compared with prior measurements of colloids containing 0.02% Pluronic
F108. The dielectric and conductivity of the 0.02% Pluronic colloids rose linearly with carbon concentration but the
0.32% Pluronic colloids varied from linearity.
Based on the permittivity results, selected colloid samples were placed inside a Bruker 7T/20 magnetic resonance (MR)
imaging (MRI) system and irradiated at 300 MHz using a high duty cycle RF pulse sequence. The temperature changes
were measured directly using fiber-optic thermometers and indirectly using MR thermometry and spectroscopy.
Temperature changes were consistent with the colloid conductivities.
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