Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Acoustic, psychophysical, and neuroimaging measurements of the effectiveness of active cancellation during auditory functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is one of the principal neuroimaging techniques for studying human audition, but it generates an intense background sound which hinders listening performance and confounds measures of the auditory response. This paper reports the perceptual effects of an active noise control (ANC) system that operates in the electromagnetically hostile and physically compact neuroimaging environment to provide significant noise reduction, without interfering with image quality. Cancellation was first evaluated at 600 Hz, corresponding to the dominant peak in the power spectrum of the background sound and at which cancellation is maximally effective. Microphone measurements at the ear demonstrated 35 dB of acoustic attenuation [from 93 to 58 dB sound pressure level (SPL)], while masked detection thresholds improved by 20 dB (from 74 to 54 dB SPL). Considerable perceptual benefits were also obtained across other frequencies, including those corresponding to dips in the spectrum of the background sound. Cancellation also improved the statistical detection of sound-related cortical activation, especially for sounds presented at low intensities. These results confirm that ANC offers substantial benefits for fMRI research

One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense