Inverse Tone Mapping Using Edge-Guided Luminance Expansion
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Solution Overview
Problem
Existing methods for expanding low dynamic range (LDR) images to high dynamic range (HDR) often increase noise and artifacts, making them more visible and disturbing, especially when additional denoising processes require significant computational resources.
Innovation Solution
A method that inversely tone maps luminance values based on edge and gradient information around pixels, using a weighted combination of low-pass filtered and original luminance, to reduce noise while maintaining image details, without requiring extensive additional processing steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If luminance expansion is applied to convert LDR images to HDR, then the dynamic range and contrast are improved, but noise and artifacts are amplified and become more visible
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. Edge pixels receive different treatment than non-edge pixels, with the processing adapting to local gradient information to preserve details while reducing noise in appropriate regions.
Solution Approach 2:
The patent modifies the expansion exponent parameter dynamically based on local image characteristics. By computing an adaptive expansion exponent that varies with local gradient magnitude, the method adjusts the expansion strength locally to minimize noise amplification while maintaining contrast where needed.
2Object-affected harmful factors
If additional denoising processes are applied to reduce noise, then noise visibility is reduced, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent combines the denoising function with the inverse tone mapping operation itself. Rather than applying separate denoising steps before or after ITMO, the method integrates noise reduction into the luminance expansion process by using gradient information to guide the expansion, thereby achieving both objectives in a unified operation.
Solution Approach 2:
The inverse tone mapping process itself provides the denoising effect through its inherent use of gradient information. The algorithm automatically adapts its behavior based on local image characteristics, performing both expansion and noise reduction functions within the same computational framework without requiring external denoising operations.
3Object-affected harmful factors
If gradient-based adaptive expansion is used, then noise reduction is improved, but the computational steps increase
Solution Approach 1:
The patent computes gradient information and determines adaptive expansion parameters in advance, before applying the full expansion operation. By pre-calculating the expansion exponent based on gradient magnitude, the method prepares noise-reduction-guided parameters that streamline the subsequent expansion process rather than adding iterative refinement steps.
Data Source
AI summary
A method for inverse tone mapping is provided. The method comprising obtaining a digital image in a color space wherein the luminance is separate from the chrominance, determining a base luminance of pixels in the digital image, determining a detail enhancement map, determining a pixel expansion exponent map, determining an edge map of the image, inverse tone mapping luminance of image based on edge map, pixel expansion map and base luminance, and providing an expanded dynamic range image based on the inverse tone mapped luminance.