1. High Resolution Computational Imaging based on Camera 2.0
We are interested in high resolution computational imaging with the following properties
- Methods to refocus and estimate depth from computational (plenoptic) camera and Camera 2.0 platform
- Methods to get high resolution output image and/or depth from plenoptic camera.
- Methods to operate computational camera in low power-consumption
- When implementing plenoptic camera as very thin camera module, are there any problem expected?
- There may be trade-off between output resolution and refocusing performance. How can we increase output resolution while keeping refocusing performance?
- Can super-resolution techniques increase output resolution? Are there any artifact or resource limitation from super-resolution?
Subject 2: Development of High Performance in Sub-1um Pixel and Simulation Environment
Challenges that significantly advance the state-of-the-art in pixel technologies include:
- New microlens structure to reduce diffraction and enhance light gathering efficiency in the submicron pixel sensor
- New methods to enhance light absorption power in the submicron pixel sensor
- New methods include new material as well as new optical structure
- Novel concepts (e.g. surface plasmon, multiple electron generation, etc.) are also welcomed.
- New color filter material and structure to improve SNR with good color accuracy
- Simulation method to increase speed and accuracy
- How can we control the diffraction of the lens between pixels to reduce the crosstalk in the submicron pixel sensor?
- How can the light gathering power of microlens be improved in the sub-micron pixels?
- How to remove loss of power in sub-micron pixels?
- Additional Structures to enhance the absorption power in the submicron pixel sensor.
- What is the ideal color filter spectrum to improve SNR?
- What is the best simulation method to improve speed and accuracy?
Subject 3: Energy Efficient Column Parallel Two Step ADCs for High Speed Imaging
We are interested in a two step ADC regarding the embodiment of low power & high speed CIS:
- Optimization of CIS readout architecture to overcome the two step ADC’s weakness
- ADC type to improve the productivity as well as size, speed, and power
- Structure innovation for energy-efficient two-step ADC having ultra low noise
- How can we get over all the obstacles, especially the trade-off relation between power, speed, and noise when designing a next CIS ADC?
- How can we secure the uniformity and productivity as well as IP’s performance?
- How can we get over the size competitiveness of original single slope ADC as well as power efficiency?
Subject 4: Resolution Enhancement of Image from Low Resolution Image
Challenges that significantly advance the state-of-the-art in image upscaling technologies include:
- Image upscaling based on self-similarity of an input image.
- Reducing artifacts and improve naturalness of the upscaled image
- Reducing required line memory and computational complexity of upscaling algorithm
- How to reduce artificial and unnatural representation of upscaled image to complex textured input image?
- How to define similarity among similar patterns? If we have similarity measure, how to use this similarity to stitch and upscale high resolution image?
- How to reduce computational redundancy in cascaded processing of self-similarity based upscaling?
Subject 5: Smart Image Sensor
- Advanced smart functional imaging technologies, especially in the field of health-care, natural user interface, virtual reality, etc.
- Pixel, circuit, image signal processing, optics, module and any other system level architectures covering the above mentioned area.
- Unprecedented pixel, circuit, and system level core technologies, such as three dimensional imaging, cognitive imaging, imaging in non-visible wavelength range, infrared-to-visible converting imaging, single transistor CMOS imaging, etc.
- Image signal processing algorithm which accounts for effectiveness in smart functionalities, such as smart pattern and motion recognition, etc.
- Methodology of analysis and characterization in pixel and system level for advanced smart imaging devices.
- Why do the new smart functionalities of proposition bear technological impact and possibly open new consumer electronics markets in regards of image sensor?
- How can the proposition be realized with new architectures?
- How can the proposition be realized in practice? For instance, is the proposition achievable with current CMOS technologies? Would the power of electrical consumption and operational speed be acceptable?
- Why do the methodology of analysis and characterization of proposition bear academic and technological importance and effectiveness?
Subject 6: Si Photonic Biosensor for Healthcare
- Smart biosensor technologies, especially in the area of disease detection such as cancer, virus, glucose and DNA sequencing for health-care
- Biosensing element and bio-processing, circuit and system level architectures covering the above mentioned area.
- Biomarker discovery for lung cancer diagnosis
- Photonics integrated circuits for biosensor, such as micro-optical spectrometer, WDM devices and optical ring resonator, etc.
- Circuit architectures, such as resonant wavelength sensing readout with low noise, etc.
- Methodology of analysis and characterization in bio-processing and system level for advanced smart biosensor.
- Measurement of the shift in resonant wavelength
- Miniaturized photonic components and biosensor element
- Compatibility with standard CMOS process
- Bio data processing algorithm which accounts for effectiveness in detection and DNA sequencing.
- Why the new functionalities of proposition should be realized with new architecture?
- How effectively the proposition can be realized in practice?
Interesting to note that 2012 is the first year program having a large image sensor section. The previous years programs had no CIS content.