Discrete Wavelet Transform Image Processing System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data enhancement systems in image and video processing are computationally complex, leading to slow processing speeds and unacceptable latency, particularly in real-time applications, and existing solutions to improve speed are either costly or insufficiently effective.
Innovation Solution
An image processing system with multiple data enhancement processing units connected via a communication bus, utilizing a discrete wavelet transform to decompose data into wavelet coefficients, applying multiple processing techniques, including frequency-based methods, to transform and enhance images efficiently, reducing computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple processing techniques are applied to enhance image data, then image enhancement performance is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies discrete wavelet transform to decompose the image into different frequency components (wavelet coefficients). This segmentation allows different processing techniques to be applied to different frequency bands independently, reducing the computational complexity of applying multiple enhancement techniques while maintaining overall enhancement performance.
Solution Approach 2:
The patent transforms the image from the spatial domain to the frequency domain using wavelet transform. This dimensional change enables frequency-based processing techniques to be applied efficiently, as operations in the frequency domain can be performed with lower computational complexity compared to spatial domain operations.
2Manufacturing precision
If multiple processing techniques are applied to enhance image data, then image enhancement performance is improved, but processing speed decreases
Solution Approach 1:
By segmenting the image into frequency components through wavelet transform, the patent enables parallel processing of different frequency bands. This segmentation allows multiple enhancement techniques to be applied simultaneously to different parts of the transformed data, maintaining high enhancement performance while improving processing speed.
Solution Approach 2:
The transformation to the frequency domain enables more efficient processing algorithms to be applied. Frequency-based operations can be performed with lower computational complexity, allowing multiple enhancement techniques to be applied faster compared to traditional spatial domain processing.
3Manufacturing precision
If conventional data enhancement methods are used, then image enhancement is achieved, but power consumption increases
Solution Approach 1:
The wavelet transform segments the image data into frequency components, allowing power-efficient processing by applying enhancement techniques only to the necessary frequency bands. This segmentation enables selective processing that reduces overall power consumption compared to processing the entire image with all enhancement techniques.
Solution Approach 2:
The frequency domain transformation enables more energy-efficient algorithms to be applied. Operations in the frequency domain require less computational power than equivalent spatial domain operations, thereby reducing power consumption while maintaining image enhancement capability.
Data Source
AI summary
The present invention relates to improved systems and methods of image processing and more particularly to improved systems and method of image processing using modified image data to produce enhanced data and images using fewer processing cycles and lower system power.