Image processing apparatus, image processing method, and display apparatus based on the same

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.

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.

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.