1. An electrowetting system for the movement of a droplet regardless of voltage polarity, the system comprising:
an electrode layer adapted to act as a resistor in the electrowetting system;
a dielectric layer disposed in overlying relation to the electrode layer, the dielectric layer adapted to act as a capacitor in the electrowetting system; and
a plurality of diodes disposed within the dielectric layer, the plurality of diodes adapted to act as diodes in parallel with the dielectric layer.
2. The electrowetting system of claim 1, further comprising a hydrophobic surface treatment layer disposed in overlying relation to the dielectric layer, the hydrophobic surface treatment layer adapted to act as a capacitor in parallel with the dielectric layer and the plurality of diodes.
3. The electrowetting system of claim 2, further comprising an electrolyte droplet disposed in overly relation to the hydrophobic surface treatment layer.
4. The electrowetting system of claim 3, wherein the plurality of diodes disposed in the dielectric are spaced apart such that at least two of the diodes are simultaneously covered by the droplet.
5. The electrowetting system of claim 1, wherein the electrode layer is a doped silicon wafer.
6. The electrowetting system of claim 1, wherein the dielectric layer is silicon dioxide.
7. The electrowetting system of claim 1, wherein the plurality of diodes are electrochemical diodes.
8. The electrowetting system of claim 1, wherein the plurality of diodes are electrochemical diodes comprising a metal electrode exposed to an aqueous solution.
9. The electrowetting system of claim 1, wherein each of the plurality of diodes comprises an aluminum electrode exposed to an aqueous solution comprising compounds selected from the group consisting of NaCl, NaSO4, Citric Acid, and Tartaric acid.
10. The electrowetting system of claim 1, wherein the plurality of diodes are metal-semiconductor diodes.
11. The electrowetting system of claim 1, wherein the electrode layer is a semiconductor layer and wherein each of the plurality of diodes are metal-semiconductor diodes comprising a metal electrode.
12. The electrowetting system of claim 11, wherein the metal electrode is selected from the group of metals consisting of gold (Au), platinum (Pt), silver (Ag), copper (Cu), aluminum (Al) and titanium (Ti).
13. The electrowetting system of claim 11, wherein the semiconductor layer is a doped silicon layer.
14. The electrowetting system of claim 11, wherein the semiconductor layer is and n-type silicon layer comprising a dopant selected from the group consisting of phosphorus (P), silicon carbide (SiC), and gallium arsenide (GaAs).
15. The electrowetting system of claim 1, wherein the plurality of diodes are Schottky diodes.
16. The electrowetting system of claim 1, wherein a first plurality of the plurality of diodes are electrochemical diodes and a second plurality of the plurality of diodes are metal-semiconductor diodes.
17. The electrowetting system of claim 1, wherein each of the plurality of diodes comprises a valve metal electrode.
18. The electrowetting system of claim 2, wherein the hydrophobic surface treatment layer is a fluoropolymer.
19. An electrowetting system for the movement of a droplet regardless of voltage polarity, the system comprising:
an electrode layer adapted to act as a resistor in the electrowetting system;
a dielectric layer disposed in overlying relation to the electrode layer, the dielectric later adapted to act as a capacitor is the electrowetting system;
a plurality of diodes disposed within the dielectric layer, the plurality of diodes adapted to act as diodes in parallel with the dielectric layer;
a hydrophobic surface treatment layer disposed in overlying relation to the dielectric layer, the hydrophobic surface treatment layer adapted to act as a capacitor in parallel with the dielectric layer and the plurality of diodes;
an electrolyte droplet disposed in overly relation to the hydrophobic surface treatment layer; and
a voltage source, coupled to the electrode layer, the voltage source to establish a potential difference between the plurality of diodes, whereby said electrolyte droplet moves due to the potential difference between the diodes.
20. A method of effecting movement of an electrolyte droplet on the surface of an electrowetting system comprising an electrode layer adapted to act as a resistor in the electrowetting system, a dielectric layer disposed in overlying relation to the electrode layer, the dielectric layer adapted to act as a capacitor in the electrowetting system and a plurality of diodes disposed within the dielectric layer, the plurality of metal-semiconductor diodes adapted to act as diodes in parallel with the dielectric layer, the method comprising:
positioning an electrolyte drop on the surface of the electrowetting system and in overlying relation to two of the plurality of diodes; and
applying a voltage source across the electrode layer to establish a potential difference between the plurality of diodes, whereby movement of the electrolyte droplet is effected as a result of the potential difference between the plurality of diodes.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. An image processor for processing an input image to an output image, comprising:
(a) an image input device for reading an input image, wherein said input image has a color filter array (CFA) mosaic structure;
(b) a storage device for containing a set of learned regression coefficients for a predefined estimation function for estimating output colorbands at image pixels, wherein each of said set of said learned regression coefficients is defined for a collection of nearby pixels for each of said image pixels; and
(c) a pixel processing module for processing each of the pixels of said input image, wherein said pixel processor:
(i) obtains said collection of nearby pixels for each of said pixels in said input image,
(ii) selects learned regression coefficients from said stored learned regression coefficients based on the position of said pixel in said color filter array mosaic structure and a calculation using said collection of nearby pixels in said input image, and
(iii) applies said learned regression coefficients and said predefined estimation function to said pixels in said input image, thereby processing said input image to an output image,
wherein said output image contains for each pixel values for a specified number of colorbands, which are different or the same in number or spectral sensitivity from said colorbands of said input image.
2. The image processor as set forth in claim 1, wherein said learned regression coefficients are obtained from a learning process comprising a learning regression method using a set of training images comprising pairs of input and desired output images.
3. The image processor as set forth in claim 2, wherein said learned regression coefficients are obtained using a Wiener filter.
4. The image processor as set forth in claim 1, wherein said estimation function is a linear function and learned regression coefficients represent an optimal convolution filter for the respective collection of nearby pixels for each of said image pixels.
5. The image processor as set forth in claim 1, wherein said pixel processor module, before applying said predefined estimation function, comprises a module for reordering said collection of nearby pixels in said input image based on values of said pixels in said input image.
6. The image processor as set forth in claim 5, wherein said reordering module comprises a module for implementing rotating andor mirroring of values of said pixels in said input image.
7. The image processor as set forth in claim 1, wherein said selection of stored learned regression coefficients is determined by calculation of luminance, noise level, degree of aberration, position in image, amount of saturation, presence of erroneous pixels, amount of contrast, average value in each colorband, or any combination thereof.
8. An image processor for processing an input image to an output image, comprising:
(a) an image input device for reading an input image, wherein said input image has a color filter array (CFA) mosaic structure;
(b) a storage device for containing a set of learned regression coefficients for a predefined estimation function for estimating output colorbands at image pixels, wherein each of said set of said learned regression coefficients is defined for a primary collection of nearby pixels for each of said image pixels; and
(c) a pixel processing module for processing each of said pixels of said input image, wherein said pixel processor:
(i) applies a set of predefined global filters to said input image,
(ii) obtains said primary collection of nearby pixels of said input image for said pixel;
(iii) selects learned regression coefficients from said stored learned regression coefficients based on the position of said pixel in said color filter array mosaic structure and a secondary collection of nearby pixels from the result of applying said global filters; and
(iv) applies said learned regression coefficients and said predefined estimation function to said pixels, thereby processing said input image to an output image,
wherein said output image contains for each pixel values for a specified number of colorbands, which are different or the same in number or spectral sensitivity from said colorbands of said input image.
9. The image processor as set forth in claim 8, wherein said learned regression coefficients are obtained from a learning process comprising a learning regression method using a set of training images comprising pairs of input and desired output images.
10. The image processor as set forth in claim 8, wherein said learned regression coefficients are obtained using a Wiener filter.
11. The image processor as set forth in claim 8, wherein said estimation function is a linear function and learned regression coefficients represent an optimal convolution filter for said respective primary collection of nearby pixels for each of said image pixels.
12. The image processor as set forth in claim 8, wherein said pixel processor module, before applying said predefined estimation function, comprises a module for reordering said primary collection of nearby pixels in said input image based on values of said pixels in said input image.
13. The image processor as set forth in claim 12, wherein said reordering module comprises a module for implementing rotating andor mirroring of values of said pixels in said input image.
14. The image processor as set forth in claim 8, wherein said predefined global filters are based on a calculation of luminance, noise level, degree of aberration, position in image, amount of saturation, presence of erroneous pixels, amount of contrast, average value in each colorband, or any combination thereof.
15. An image processor for processing an input image to an output image, comprising:
(a) an image input device for reading an input image;
(b) a storage device for containing a set of learned regression coefficients for a predefined estimation function for estimating output colorbands at image pixels of said input image, wherein each of said set of said learned regression coefficients is defined for a collection of nearby pixels for each of said image pixels; and
(c) a pixel processor module for processing each of said pixels of said input image, wherein said pixel processor obtains said collection of nearby pixels for said pixel, and wherein said pixel processor:
(i) reorders said collection of nearby pixels;
(ii) selects learned regression coefficients from said stored learned regression coefficients based on said reordered collection of said nearby pixels; and
(iii) applies said learned regression coefficients and said estimation function to said pixels, thereby processing said input image to an output image,
wherein said estimated colorbands at said output image pixels are different or the same in number or spectral sensitivity from said colorbands of said input image.
16. The image processor as set forth in claim 15, wherein said learned regression coefficients are obtained from a learning process comprising a learning regression method using a set of training images comprising pairs of input and desired output images.
17. The image processor as set forth in claim 15, wherein said learned regression coefficients are obtained using a Wiener filter.
18. The image processor as set forth in claim 15, wherein said estimation function is a linear function and learned regression coefficients represent an optimal convolution filter for the respective collection of nearby pixels for each of said image pixels.
19. The image processor as set forth in claim 15, wherein said reordering module comprises a module for implementing rotating andor mirroring of values of said pixels in said input image.
20. The image processor as set forth in claim 15, wherein said selection of stored learned regression coefficients is determined by calculation of luminance, noise level, degree of aberration, position in image, amount of saturation, presence of erroneous pixels, amount of contrast, average value in each colorband, or any combination thereof.