Inverse Exponential Noise Reduction for Edge Preservation

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Solution Overview

Problem

Existing edge preserving noise reduction algorithms, such as those using bilateral and guide filters, face high computational complexity and require numerous configuration parameters, making them difficult to deploy on intensive computing accelerators and limiting their effectiveness in maintaining object edges while reducing noise.

Innovation Solution

An edge preserving noise reduction algorithm utilizing inverse exponential function optimization, implemented as an iterative process with lower computational complexity and fewer configuration parameters, which calculates gradient vectors and applies inverse exponential transformations to reduce noise while preserving object edges.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Object-affected harmful factors

If traditional Gaussian smooth or average smooth filtering is used to reduce noise, then noise suppression is improved, but object edges are blurred

Engineering Contradiction:

Improvenoise suppressionVSAvoidobject edge sharpness

Core Design Contradiction:

Object-affected harmful factorsVSShape

Solution Approach 1:

The patent applies different processing characteristics to different regions of the image by computing gradient vectors and using inverse exponential function optimization. The filtering strength varies locally based on gradient magnitude, allowing strong noise suppression in flat regions while preserving edges where gradient is high.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent transforms the static filtering problem into a dynamic iterative optimization process. The filter adapts its behavior through multiple iterations, adjusting the transformation based on computed gradient vectors and inverse exponential functions to dynamically balance noise suppression and edge preservation.

Inventive Principle:

Principle #15Dynamics

2Shape

If bilateral filter is used to preserve edges during noise reduction, then edge preservation is improved, but computational complexity increases due to non-linear filter kernel

Engineering Contradiction:

Improveobject edge preservationVSAvoidcomputational complexity

Core Design Contradiction:

ShapeVSDevice complexity

Solution Approach 1:

The patent replaces the complex non-linear filter kernel mechanism of the bilateral filter with a gradient-based optimization approach using inverse exponential functions. This substitution maintains edge preservation capability while reducing computational complexity to enable hardware acceleration.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameters and approach from using a non-linear filter kernel with multiple parameters to a gradient-based method with fewer parameters. The inverse exponential function optimization uses gradient magnitude and direction as key parameters, simplifying the computational model.

Inventive Principle:

Principle #35Parameter changes

3Shape

If guide filter is used to preserve edges, then edge preservation is improved, but adaptability decreases due to requirement of setting parameters for variant image scenarios

Engineering Contradiction:

Improveobject edge preservationVSAvoidparameter adaptability

Core Design Contradiction:

ShapeVSAdaptability or versatility

Solution Approach 1:

The patent implements a self-adaptive mechanism where the filter automatically adjusts its behavior based on local image characteristics computed through gradient vectors. The inverse exponential function optimization inherently adapts to different image scenarios without requiring manual parameter setting, making the system self-service and highly adaptable.

Inventive Principle:

Principle #25Self-service

4Object-affected harmful factors

If bilateral filter or guide filter is used for noise reduction, then noise suppression is improved, but deployment difficulty increases due to large amounts of non-linear calculations

Engineering Contradiction:

Improvenoise suppressionVSAvoidhardware deployment ease

Core Design Contradiction:

Object-affected harmful factorsVSEase of manufacture

Solution Approach 1:

The patent replaces the complex non-linear calculation system with a gradient-based optimization system using inverse exponential functions that is more amenable to hardware implementation. The substitution enables deployment on intensive computing accelerators by reducing the computational burden while maintaining noise suppression effectiveness.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11521300B2Edge preserving noise reduction algorithm using inverse exponential function optimization

Publication Date: 2022.12.06 AMBARELLA INT LP

AI summary

An apparatus includes a memory and a processing circuit. The memory may be configured to store image data of an image. The processor circuit may be configured to (a) copy the image data of the image from the memory to a first memory buffer of the processor circuit, (b) calculate first vector values for each pixel location in the image using the image data stored in the first memory buffer, (c) calculate second vector values for each pixel location in the image using the image data stored in the first memory buffer and the first vector values, (d) transform the image data stored in the first memory buffer by adding the second vector values to corresponding image data, (e) storing the transformed image data to the memory, and (f) repeating steps (a) through (e) until the image data of the image has been transformed.