Tone Based Non-Smooth Detection for Image Noise Reduction
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Solution Overview
Problem
Conventional digital image processing techniques struggle to effectively reduce noise without distorting the image, as they cannot accurately determine true pixel values, leading to subjective quality degradation, especially in smooth areas where noise is more visible.
Innovation Solution
A tone-based non-smooth detection method is implemented in a raw image pipeline using a first circuit to distinguish between smooth and non-smooth areas, adjusting noise reduction and sharpening filters based on adjusted non-smoothness values derived from tone values, allowing for improved noise correction and image quality preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise reduction techniques are applied to reduce picture noise, then noise visibility is reduced, but the true pixel values are distorted and subjective picture quality degrades
Solution Approach 1:
The patent applies different processing strengths to different regions of the image based on local characteristics. It calculates a non-smoothness measure for each pixel neighborhood and uses this to adaptively control the amount of noise reduction applied, preserving edges and textures while reducing noise in smooth areas.
Solution Approach 2:
The patent changes the processing parameters dynamically based on local image characteristics. The non-smoothness measure serves as a parameter that controls the strength of noise reduction filtering, allowing the system to adapt to different regions rather than applying a fixed processing strength globally.
2Manufacturing precision
If sharpening is applied to improve picture quality, then image detail is enhanced, but noise perception increases and subjective quality degrades in smooth areas
Solution Approach 1:
The patent applies sharpening operations selectively based on local non-smoothness characteristics. Areas with high non-smoothness values (edges, textures) receive stronger sharpening, while smooth areas with low non-smoothness values receive minimal or no sharpening, preventing noise amplification in uniform regions.
Solution Approach 2:
The sharpening strength is made dynamic and adaptive rather than fixed. The system continuously evaluates the non-smoothness measure across the image and adjusts sharpening parameters accordingly, creating a dynamic processing approach that responds to local image content.
3Object-affected harmful factors
If uniform noise reduction is applied across the entire image, then noise is reduced globally, but distinction between smooth noisy areas and non-smooth clean areas is lost
Solution Approach 1:
The patent calculates a non-smoothness measure for each pixel's neighborhood, enabling local differentiation between smooth and non-smooth areas. This local analysis allows the system to identify and treat different regions with appropriate processing strengths, preserving area differentiation while reducing noise.
Solution Approach 2:
The patent performs preliminary analysis of image characteristics by calculating non-smoothness measures before applying noise reduction. This pre-processing step identifies smooth areas that benefit from noise reduction while preserving non-smooth areas, enabling targeted processing that maintains area distinction.
Data Source
AI summary
An apparatus includes a raw image pipeline comprising a first circuit and a second circuit. The first circuit of the raw image pipeline may be configured to distinguish between smooth picture noisy areas and non-smooth clean areas of an image by performing tone based non-smooth detection on data of the image to obtain an adjusted non-smoothness value for at least one area comprising a plurality of pixels of the image. The second circuit of the raw image pipeline may be configured to adjust one or more of noise reduction filtering or sharpening filtering performed on the at least one area of the image based on the adjusted non-smoothness value.