Effects of apodization on a holographic demultiplexer based on a photopolymer grating

An investigation of the effects of apodization on a holographic demultiplexer that is based on a photopolymer grating is presented. Uniform and Gaussian apodized gratings are fabricated in a DuPont HRF-150-38 photopolymer. From the theoretical and experimental results, the spectral response of the apodized grating has a larger main lobe but lower sidelobes than those in the uniform-grating case. A 42-channel demultiplexer that is based on the Gaussian apodized grating with an 0.4-nm channel spacing is demonstrated. A cross-talk level of -30 dB and an interchannel uniformity of 1.5 dB are archived in the wavelength range of approximately 1550 nm.     

Image super resolution based on residual dense CNN and guided filters

Convolutional neural networks (CNNs) have recently made impressive results for image super-resolution (SR). Our goal is to introduce a new image SR framework rely on a CNN. In this paper, the input image is decomposed into luminance channel and chromatic channels. A designed network based on a residual dense network is introduced to extract the hierarchical features from luminance part. The bicubic interpolation is simply used to upscale low resolution (LR) chromatic channels. However, this step degrades the chromatic channels. To tackle this issue, the SR reconstructed luminance channel is applied as the reference image in guided filters to promote the interpolated chromatic channels. Guided filters technique has ability to retain sharp edges and fine details from the reference image and carry them to the target images. Extensive experiments on several commonly used image SR testing datasets demonstrate that our framework has the ability to extract features and outperforms existing well-known techniques for image SR by LR image into the high resolution (HR) image efficiently.