Method and Device for Adaptively Removing Noise from an Image

• US 20110235938A1
• Filed: 03/22/2011
• Published: 09/29/2011
• Est. Priority Date: 03/29/2010
• Status: Abandoned Application

• Current Assignee: Novatek Microelectronics Corporation (Novatek Microelectronics Corp Limited)
• Original Assignee: Novatek Microelectronics Corporation (Novatek Microelectronics Corp Limited)
• Inventors: Lin, Yu-Mao
• Application Number:
  US13/053,214
• Publication Number:
  US 20110235938A1
• Time in Patent Office: Days
• US Class Current: 382/264
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

An image processing method for adaptively removing noise from an image is disclosed. The image processing method includes computing a plurality of gradients for one of a plurality of pixels of the image, determining an edge level and an edge direction of the pixel according to the plurality of gradients, selecting a plurality of nearby pixels from the plurality of pixels according to the edge level and the edge direction, computing a plurality of likelihoods between the pixel and the plurality of nearby pixels, generating a plurality of weights according to the plurality of likelihoods, and applying weighted low-pass filtering to the plurality of nearby pixels and the pixel according to the plurality of weights to generate an output pixel.

Claims

1. An image processing method for adaptively removing noise from an image, the image processing method comprising:

computing a plurality of gradients corresponding to a plurality of directions for one of a plurality of pixels of the image;

determining an edge level and an edge direction of the pixel according to the plurality of gradients;

selecting a plurality of nearby pixels around the pixel from the plurality of pixels according to the edge level and the edge direction;

computing a plurality of likelihoods between the pixel and the plurality of nearby pixels;

generating a plurality of weights according to the plurality of likelihoods; and

applying weighted low-pass filtering to the plurality of nearby pixels and the pixel according to the plurality of weights to generate an output pixel.

2. The image processing method of claim 1, wherein the plurality of directions comprises a first direction and a second direction orthogonal to each other.

3. The image processing method of claim 1, wherein the step of selecting the plurality of nearby pixels around the pixel from the plurality of pixels according to the edge level and the edge direction comprises selecting parts of the plurality of pixels horizontally nearby the pixel to be the plurality of nearby pixels when the edge direction is a horizontal direction.

4. The image processing method of claim 1, wherein the step of selecting the plurality of nearby pixels around the pixel from the plurality of pixels according to the edge level and the edge direction comprises selecting parts of the plurality of pixels vertically nearby the pixel to be the plurality of nearby pixels when the edge direction is a vertical direction.

5. The image processing method of claim 1, wherein the step of selecting the plurality of nearby pixels around the pixel from the plurality of pixels according to the edge level and the edge direction comprises averagely selecting parts of the plurality of pixels around the pixel to be the plurality of nearby pixels when the edge direction is insignificant.

6. The image processing method of claim 1, wherein the step of computing the plurality of likelihoods between the pixel and the plurality of nearby pixels comprises computing a plurality of absolute values of a plurality of reciprocals of a plurality of grey-level differences between the plurality of nearby pixels and the pixel to be the plurality of likelihoods.

7. The image processing method of claim 1, wherein the step of generating the plurality of weights according to the plurality of likelihoods comprises maintaining the weight to be a standard weight when the weight corresponds to a high likelihood of the plurality of likelihoods.

8. The image processing method of claim 1, wherein the step of generating the plurality of weights according to the plurality of likelihoods comprises reducing the weight when the weight corresponds to a low likelihood of the plurality of likelihoods.

9. An image processing device for adaptively removing noise from an image, the image processing device comprising:

a reception end, for receiving a plurality of pixels of the image;

an output end, for outputting an output pixel;

an edge detector, comprising;

at least one gradient detector, for computing a plurality of gradients corresponding to a plurality of directions for one of the plurality of pixels; and

a gradient analyzer, for determining an edge level and an edge direction of the pixel according to the plurality of gradients;

a pixel delay unit, for delaying the plurality of pixels to be synchronized with the edge level and the edge direction;

a pixel selector, for selecting a plurality of nearby pixels around the pixel from the plurality of pixels according to the edge level and the edge direction; and

an adaptive low-pass filtering device, comprising;

a likelihood computing unit, for computing a plurality of likelihoods between the pixel and the plurality of nearby pixels;

a weight generator, for generating a plurality of weights according to the plurality of likelihoods; and

a low-pass filter, for applying weighted low-pass filtering to the plurality of nearby pixels and the pixel according to the plurality of weights to generate the output pixel.

10. The image processing device of claim 9, wherein the plurality of directions comprises a first direction and a second direction orthogonal to each other.

11. The image processing device of claim 9, wherein the pixel selector selects parts of the plurality of pixels horizontally nearby the pixel to be the plurality of nearby pixels when the edge direction is a horizontal direction.

12. The image processing device of claim 9, wherein the pixel selector selects parts of the plurality of pixels vertically nearby the pixel to be the plurality of nearby pixels when the edge direction is a vertical direction.

13. The image processing device of claim 9, wherein the pixel selector averagely selects parts of the plurality of pixels around the pixel to be the plurality of nearby pixels when the edge direction is insignificant.

14. The image processing device of claim 9, wherein the likelihood computing unit computes a plurality of absolute values of a plurality of reciprocals of a plurality of grey-level differences between the plurality of nearby pixels and the pixel to be the plurality of likelihoods.

15. The image processing device of claim 9, wherein the weight generator maintains the weight to be a standard weight when the weight corresponds to a high likelihood of the plurality of likelihoods.

16. The image processing device of claim 9, wherein the weight generator reduces the weight when the weight corresponds to a low likelihood of the plurality of likelihoods.