Dynamic Range Compression with Local Contrast Enhancement
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Solution Overview
Problem
Current image processing algorithms, such as AINDANE, require high computational costs and memory for dynamic range compression and local contrast enhancement, leading to inefficient processing and over-enhancement of dark regions in images.
Innovation Solution
A method and device for dynamic range compression with local contrast enhancement using a non-linear intensity-transfer function and a dynamic range compression algorithm that incorporates a luminance remapping process, along with a local contrast enhancement component, to enhance image brightness and detail while preserving color, utilizing a filter computation and parameter computation to optimize pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the AINDANE algorithm is used for dynamic range compression and local contrast enhancement, then image quality is improved, but computational cost and memory usage increase significantly
Solution Approach 1:
The patent extracts and processes only the luminance component of the input image separately from the color information. By applying dynamic range compression and local contrast enhancement algorithms solely to the luminance channel, the computational complexity is significantly reduced while maintaining image quality, as color information is preserved without requiring intensive processing.
Solution Approach 2:
The image processing is segmented into distinct components: luminance extraction, dynamic range compression, local contrast enhancement, and color restoration. This segmentation allows each component to be optimized independently, reducing overall computational burden while achieving high-quality results through specialized processing of each image component.
2Manufacturing precision
If the AINDANE algorithm is used for dynamic range compression and local contrast enhancement, then image quality is improved, but processing speed decreases due to high computational costs
Solution Approach 1:
By extracting and processing only the luminance component, the patent reduces the amount of data requiring intensive computation. This extraction approach maintains image quality through specialized luminance processing while significantly improving processing speed by avoiding redundant computation on color channels.
Solution Approach 2:
The patent transforms the input luminance image into an intermediate representation using a power-law transformation with a可调 exponent parameter. This parameter change creates a compressed dynamic range that requires less computational effort for subsequent contrast enhancement operations, thereby improving processing speed while preserving image quality.
3Manufacturing precision
If the AINDANE algorithm is used for local contrast enhancement, then dark region detail is enhanced, but over-enhancement occurs leading to artifacts
Solution Approach 1:
The patent employs a feedback mechanism where the enhanced luminance image is combined with the original input image using a weighted average. The weighting factor is adaptively determined based on local image characteristics, providing feedback control that prevents over-enhancement artifacts while preserving dark region details. This feedback approach ensures that enhancement is applied only where necessary and at appropriate intensities.
Data Source
AI summary
An image dynamic range compression with local contrast enhancement method for an image processing device is provided. The method includes the following steps. A plurality of input pixels of an image including a first input pixel are received, and an input luminance pixel value of each of the input pixels as well as a darkness intensity level of the image are obtained. A filter result of the first input pixel is obtained according to filter computation on the input luminance pixel values; an image-related parameter is obtained according to image-related computation on the darkness intensity level. The image-related parameter, the filter result of the first input pixel, and the input luminance pixel value of the first input pixel are transformed into an output luminance pixel value of the first input pixel according to a non-linear intensity transfer function and a dynamic range compression with local contrast enhancement algorithm.