Image processing apparatus, image processing method, and display apparatus based on the same
The present disclosure provides an image processing apparatus, and image processing method, and a display apparatus. The image processing apparatus includes a first image processor up-sampling an original low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image, a second image processor interpolating the original low-resolution image to generate a second high-resolution image, a third image processor generating a difference image between the first high-resolution image and the second high-resolution image, extracting a high frequency component from the difference image, and amplifying the extracted high frequency component, and a fourth image processor adding the amplified high frequency component to the first high-resolution image to generate a target high-resolution image.
1. An image processing apparatus comprising:
a first image processor configured to up-sample an original low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image;
a second image processor configured to interpolate the original low-resolution image to generate a second high-resolution image;
a third image processor configured to generate a difference image between the first high-resolution image and the second high-resolution image, extract a high frequency component from the difference image, and amplify the extracted high frequency component; and
a fourth image processor configured to add the amplified high frequency component to the first high-resolution image to generate a target high-resolution image.
2. The image processing apparatus of claim 1 , wherein the third image processor multiplies the extracted high frequency component by a predetermined gain value to generate the amplified high frequency component.
3. The image processing apparatus of claim 1 , wherein the learning data comprises an image pair including a learning low-resolution image and a learning high-resolution image.
4. The image processing apparatus of claim 1 , wherein the third image processor further comprises a coring circuit removing noise concentrating near a threshold value of the extracted high frequency component on the basis of a predetermined coring algorithm before the extracted high frequency component is amplified.
5. An image processing apparatus comprising:
a first image processor configured to generate a denoising low-resolution image where noise is removed from an original low-resolution image;
a second image processor configured to up-sample the denoising low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image;
a third image processor configured to up-sample the original low-resolution image on the basis of the learning data to generate a second high-resolution image;
a fourth image processor configured to weighted-average the first high-resolution image and the second high-resolution image to generate a third high-resolution image;
a fifth image processor configured to interpolate the denoising low-resolution image to generate a fourth high-resolution image;
a sixth image processor configured to generate a difference image between the first high-resolution image and the fourth high-resolution image, extract a high frequency component from the difference image, and amplify the extracted high frequency component; and
a seventh image processor configured to add the amplified high frequency component to the third high-resolution image to generate a target high-resolution image.
6. The image processing apparatus of claim 5 , wherein the fourth image processor multiplies the first high-resolution image by a first weight to calculate a first weighting result, multiplies the second high-resolution image by a second weight to calculate a second weighting result, and averages the first weighting result and the second weighting result to generate the third high-resolution image, and
a sum of the first weight and the second weight is 1.
7. The image processing apparatus of claim 5 , wherein the sixth image processor multiplies the extracted high frequency component by a predetermined gain value to generate the amplified high frequency component.
8. The image processing apparatus of claim 5 , wherein the sixth image processor further comprises a coring circuit removing noise concentrating near a threshold value of the extracted high frequency component on the basis of a predetermined coring algorithm before the extracted high frequency component is amplified.
9. An image processing method comprising:
up-sampling an original low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image;
interpolating the original low-resolution image to generate a second high-resolution image;
generating a difference image between the first high-resolution image and the second high-resolution image, extracting a high frequency component from the difference image, and amplifying the extracted high frequency component; and
adding the amplified high frequency component to the first high-resolution image to generate a target high-resolution image.
10. The image processing method of claim 9 , wherein the amplifying the extracted high frequency component comprises multiplying the extracted high frequency component by a predetermined gain value.
11. The image processing method of claim 9 , further comprising removing noise concentrating near a threshold value of the extracted high frequency component on the basis of a predetermined coring algorithm before the extracted high frequency component is amplified.
12. An image processing method comprising:
generating a denoising low-resolution image where noise is removed from an original low-resolution image;
up-sampling the denoising low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image;
up-sampling the original low-resolution image on the basis of the learning data to generate a second high-resolution image;
weighted-averaging the first high-resolution image and the second high-resolution image to generate a third high-resolution image;
interpolating the denoising low-resolution image to generate a fourth high-resolution image;
generating a difference image between the first high-resolution image and the fourth high-resolution image, extracting a high frequency component from the difference image, and amplifying the extracted high frequency component; and
adding the amplified high frequency component to the third high-resolution image to generate a target high-resolution image.
13. The image processing method of claim 12 , wherein the weighted-averaging the first high-resolution image and the second high-resolution image comprises:
multiplying the first high-resolution image by a first weight to calculate a first weighting result;
multiplying the second high-resolution image by a second weight to calculate a second weighting result; and
averaging the first weighting result and the second weighting result to generate the third high-resolution image, and
a sum of the first weight and the second weight is 1.
14. The image processing method of claim 12 , wherein the amplifying the extracted high frequency component comprises multiplying the extracted high frequency component by a predetermined gain value.
15. The image processing method of claim 12 , further comprising removing noise concentrating near a threshold value of the extracted high frequency component on the basis of a predetermined coring algorithm before the extracted high frequency component is amplified.
16. A display apparatus comprising the image processing apparatus of claim 1 .
17. A display apparatus comprising the image processing apparatus of claim 5 .