Tone Mapping Integration for Image Processing Contrast and Detail
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Solution Overview
Problem
Existing image processing techniques, such as global tone mapping and local tone mapping, face challenges in balancing contrast enhancement with computational efficiency and preserving local details, often resulting in either loss of detail or high computational expense.
Innovation Solution
The integration of multiple tone control and local tone mapping techniques, where low frequency information is processed using multiple tone control based on a gain curve and high frequency information is processed using an edge preservation filter, allowing for enhanced contrast and reduced computational expense while preserving local details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If global tone mapping is used to enhance contrast, then contrast enhancement is improved, but loss of local details occurs
Solution Approach 1:
The patent divides the image into multiple blocks and processes low frequency information using multiple tone control per block while using a single global tone mapping curve. This segmentation allows local contrast enhancement without the harsh effects of traditional local tone mapping, preserving local details while improving overall contrast.
Solution Approach 2:
The patent applies different processing strategies to different parts of the image: multiple tone control is applied to low frequency information within each block for local contrast enhancement, while global tone mapping is applied to high frequency information for overall tone adjustment. This local quality approach optimizes both contrast and detail preservation.
2Loss of information
If local tone mapping is used to preserve local details, then local details are preserved, but computational expense increases
Solution Approach 1:
The patent segments the image into blocks and applies multiple tone control only to low frequency information within each block, rather than applying full local tone mapping to the entire image. This reduces the computational scope while still preserving local details where needed.
Solution Approach 2:
The patent applies multiple tone control selectively to low frequency information for local contrast enhancement, while using more efficient global tone mapping for high frequency information. This quality differentiation reduces computational expense while maintaining local detail preservation.
3Illumination intensity
If multiple tone control is applied to enhance contrast, then contrast enhancement is improved, but processing time increases
Solution Approach 1:
The patent segments the processing task by applying multiple tone control only to low frequency information within each block, rather than processing the entire image with multiple tone control. This segmentation reduces the overall processing time while still achieving effective contrast enhancement.
Solution Approach 2:
The patent applies multiple tone control locally to low frequency information for contrast enhancement, while using faster global tone mapping for high frequency information. This quality-based differentiation optimizes processing time while maintaining contrast enhancement effectiveness.
Data Source
AI summary
An image is processed using a combination of techniques, particularly, in which low frequency information is processed using multiple tone control and high frequency information is processed using local tone mapping. An image is divided into a plurality of blocks including a given block. Low frequency information and high frequency information of the given block are separated. The low frequency information is processed using multiple tone control. The high frequency information is processed using local tone mapping. A processed image is then produced based on the processed low frequency information and based on the processed high frequency information, the processed image corresponding to the image captured using the image sensor. The processed image is then output for storage or display. Processing the low frequency information can include using a gain curve and bilinear interpolation. Processing the high frequency information can include using an edge preservation filter.