DWT-Based Image Noise Reduction with Reduced Frame Memory
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Solution Overview
Problem
Existing image processing systems require large frame memory capacity for effective three-dimensional noise reduction (3DNR), which increases product costs and competitiveness issues.
Innovation Solution
An image processing apparatus and method utilizing discrete wavelet transform (DWT) and inverse DWT (IDWT) to divide source images into low-frequency and high-frequency sub-images, where only the low-frequency sub-images undergo noise reduction using a small frame memory, allowing for noise reduction in source images without the need for extensive memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a previous frame is stored to remove noise using the 3DNR method, then noise reduction effectiveness is improved, but frame memory capacity increases
Solution Approach 1:
The image is divided into frequency sub-bands using discrete wavelet transform (DWT), separating low-frequency components (which contain most image information) from high-frequency components. Only the low-frequency sub-band requires storage in frame memory for 3DNR processing, while high-frequency sub-bands are processed differently, thereby reducing the memory capacity needed while maintaining noise reduction effectiveness.
2Ease of manufacture
If frame memory capacity is reduced, then product cost and competitiveness are improved, but noise reduction effectiveness deteriorates
Solution Approach 1:
Different processing strategies are applied to different frequency sub-bands of the image. The low-frequency sub-band undergoes full 3DNR processing with frame memory storage, while high-frequency sub-bands use alternative processing methods. This localized quality approach maintains overall noise reduction effectiveness while reducing the memory resources required.
Solution Approach 2:
The essential information for noise reduction is extracted into the low-frequency sub-band, which contains the majority of image energy and structural information. By isolating and processing only this critical portion through 3DNR with reduced memory requirements, the system maintains effectiveness while lowering costs.
Data Source
AI summary
An image processing apparatus includes a discrete wavelet transform (DWT) device that performs DWT and down sampling for a first source image to divide the first source into a low-frequency sub-image including a low-frequency component in a horizontal direction and a vertical direction and a plurality of high-frequency sub-images including a high-frequency component in at least one of the horizontal direction or the vertical direction, a frame memory storing a low-frequency sub-image of a second source image, a first noise reduction device that reduces noise in the low-frequency sub-image of the first source image using the low-frequency sub-image of the second source image, and an inverse discrete wavelet transform (IDWT) device that applies IDWT to the low-frequency sub-image of the first sub-image, which is reduced in noise through the first noise reduction device, and the high-frequency sub-images of the first image to restore the first source image.