1. An image processing apparatus comprising:
a low-frequency component extraction unit for extracting a first low-frequency component from first image data;
a noise reduction unit for obtaining a second low-frequency component by applying a noise reduction filter to either the first low-frequency component or data obtained by processing the first low-frequency component;
a high-frequency component extraction unit for extracting a first high-frequency component from the first images data and either one of the second low-frequency component and data obtained by processing the second low-frequency component, and the first image data;
a high-frequency component tuning unit for obtaining a second high-frequency component by tuning the first high-frequency component to reduce amplitude of the first high-frequency component; and
an image synthesis unit for obtaining second image data by combining either one of the second low-frequency component and the data obtained by processing the second low-frequency component with the second high-frequency component.
2. The apparatus of claim 1, further comprising:
a scale-down unit for scaling down the first low-frequency component; and
a scale-up unit for scaling up the second low-frequency component,
wherein the noise reduction unit obtains a second low-frequency component by applying a noise reduction filter to the scaled-down first low-frequency component,
the high-frequency component extraction unit extracts the first high-frequency component from the scaled-up second low-frequency component and the first image data, and
the image synthesis unit obtains second image data by combining the scaled-up second low-frequency component and the second high-frequency component.
3. The apparatus of claim 1, wherein the low-frequency component extraction unit comprises a low-pass filter.
4. The apparatus of claim 1, wherein the noise reduction unit comprises a bilateral filter.
5. The apparatus of claim 1, wherein the high-frequency component extraction unit obtains the first high-frequency component by using the difference between the first image data and the second low-frequency component.
6. The apparatus of claim 2, wherein the high-frequency component extraction unit obtains the first high-frequency component by using the difference between the first image data and the scaled-up second low-frequency component.
7. An image processing method comprising:
extracting a first low-frequency component from first image data;
obtaining a second low-frequency component by applying a noise reduction filter to the first low-frequency component;
obtaining a first high-frequency component from the first image data and the second low-frequency component;
obtaining a second high-frequency component by tuning the first high-frequency component to reduce amplitude of the first high-frequency component; and
obtaining second image data by combining the second low-frequency component with the second high-frequency component.
8. The method of claim 7, wherein a low-pass filter is used in the extracting of the first low-frequency component.
9. The method of claim 7, a bilateral filter is used in the obtaining of the second low-frequency component.
10. The method of claim 7, wherein the obtaining of the first high-frequency component comprises obtaining the first high-frequency component by using the difference between the first image data and the second low-frequency component.
11. A computer readable medium having recorded thereon a program for controlling a computer to perform the following operations:
extract a first low-frequency component from first image data;
obtain a second low-frequency component by applying a noise reduction filter to the first low-frequency component;
obtain a first high-frequency component from the first image data and the second low-frequency component;
obtain a second high-frequency component by tuning the first high-frequency component to reduce amplitude of the first high-frequency component; and
obtain second image data by combining the second low-frequency component with the second high-frequency component.
12. An image processing method comprising:
extracting a first low-frequency component from first image data;
scaling down the first low-frequency component;
obtaining a second low-frequency component by applying a noise reduction filter to the scaled-down first low-frequency component;
scaling up the second low-frequency component;
obtaining a first high-frequency component from the first image data and the scaled-up second low-frequency component;
obtaining a second high-frequency component by tuning the first high-frequency component; and
obtaining second image data by combining the scaled-up second low-frequency component with the second high-frequency component.
13. The method of claim 12, wherein a low-pass filter is used in the extracting of the first low-frequency component.
14. The method of claim 12, wherein a bilateral filter is used in the obtaining of the second low-frequency component.
15. The method of claim 12, wherein the obtaining of the first high-frequency component comprises obtaining the first high-frequency component by using the difference between the first image data and the scaled-up second low-frequency component.
16. The method of claim 12, wherein the obtaining of the second high-frequency component comprises obtaining the second high-frequency component by tuning the first high-frequency component to reduce amplitude of the first high-frequency component.
17. A computer readable medium having recorded thereon a computer program for controlling a computer to perform the following operations:
extract a first low-frequency component from first image data;
scale down the first low-frequency component;
obtain a second low-frequency component by applying a noise reduction filter to the scaled-down first low-frequency component;
scale up the second low-frequency component;
obtain a first high-frequency component from the first image data and the scaled-up second low-frequency component;
obtain a second high-frequency component by tuning the first high-frequency component; and
obtain second image data by combining the scaled-up second low-frequency component with the second high-frequency component.
18. A computer readable medium of claim 17, wherein the obtaining of the first high-frequency component comprises obtaining the first high-frequency component by using the difference between the first image data and the scaled-up second low-frequency component.
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. A catalyst comprising:
a molecular sieve catalyst containing a group V metal and rhenium;
wherein the molecular sieve catalyst is a zeolite, a faujasite, a crystalline silicoaluminophosphate (SAPO), or an aluminophosphate (ALPO), and wherein the molecular sieve catalyst contains at least 0.0002 wt % and up to 0.3 wt % of the rhenium based on the total weight of the catalyst.
2. The catalyst of claim 1, wherein the rhenium comes from a rhenium precursor comprising one or more rhenium compounds.
3. The catalyst of claim 2, wherein the rhenium precursor is selected from the group consisting of water-soluble rhenium compounds.
4. The catalyst of claim 2, wherein the rhenium precursor is selected from the group consisting of sodium perrhenate, ammonium perrhenate, and dirhenium decacarbonyl.
5. The catalyst of claim 1, wherein the group V metal is niobium.
6. The catalyst of claim 5, wherein the niobium comes from a niobium precursor comprising one or more niobium compounds.
7. The catalyst of claim 6, wherein the niobium precursor is selected from the group consisting of water-soluble niobium compounds.
8. The catalyst of claim 6, wherein the niobium precursor is selected from the group consisting of niobium oxalate and ammonium niobate(V) oxalate.
9. The catalyst of claim 1, wherein the molecular sieve catalyst is the zeolite.
10. The catalyst of claim 1, wherein the molecular sieve catalyst is the crystalline silicoaluminophosphate (SAPO).
11. The catalyst of claim 1, wherein the molecular sieve catalyst is the aluminophosphate (ALPO).
12. The catalyst of claim 1, wherein the molecular sieve catalyst contains at least 0.005 wt % niobium based on the total weight of the catalyst.
13. (canceled)
14. (canceled)
15. A process for disproportionation of toluene to benzene and xylene, comprising:
passing a toluenehydrogen feedstock over a group V metalrhenium-molecular sieve catalyst at a reaction temperature ranging from 150\xb0 C. to 500\xb0 C. and a reaction pressure ranging from 200 psig to 800 psig;
wherein the molecular sieve catalyst is a zeolite, a faujasite, a crystalline silicoaluminophosphate (SAPO), or an aluminophosphate (ALPO), and wherein the molecular sieve catalyst contains at least 0.0002 wt % and up to 0.3 wt % of the rhenium based on the total weight of the catalyst.
16. The process of claim 15, wherein the molecular sieve catalyst is the faujasite.
17. The process of claim 15, wherein the group V metal content of the catalyst is from 0.005 wt % to 5.0 wt % based on the total weight of the catalyst.
18. The process of claim 15, wherein the rhenium content of the catalyst is from 0.0002 wt % to 1.0 wt % based on the total weight of the catalyst.
19. The process of claim 15, wherein the reaction temperature ranges from 300\xb0 C.-400\xb0 C.
20. The process of claim 15, wherein the molecular sieve catalyst is the crystalline silicoaluminophosphate (SAPO).
21. The process of claim 15, wherein the hydrogen:toluene molar ratio is between 0.05:1 to 4:1.
22. The process of claim 15, wherein the reaction pressure range is between 400 psig to 800 psig.
23. The process of claim 15, wherein the molecular sieve catalyst is the aluminophosphate (ALPO).
24. (canceled)
25. (canceled)