Noise Reduction Circuit Blending Bilateral and Machine Learning Kernels
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Solution Overview
Problem
Existing image processing pipelines face challenges in efficiently reducing noise in image data without consuming significant CPU bandwidth and power resources, particularly when performing noise filtering tasks.
Innovation Solution
An image processing circuit that combines machine learning (ML) and bilateral filtering techniques by generating ML and bilateral kernels for noise reduction, blending their results to produce a de-noised image, thereby optimizing noise reduction while minimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If noise filtering is performed using traditional methods on CPU, then noise reduction can be achieved, but CPU bandwidth and power consumption increase significantly
Solution Approach 1:
The patent replaces CPU-based software processing with a dedicated hardware image processing circuit that performs noise filtering. This hardware circuit includes kernel calculation circuits, noise filtering circuits, and blending circuits that operate in parallel to reduce noise without consuming CPU bandwidth or significant power, thus resolving the contradiction between noise reduction effectiveness and resource consumption.
2Productivity
If image processing is performed on CPU, then flexibility and programmability are maintained, but processing speed and efficiency decrease
Solution Approach 1:
The patent divides the image processing system into separate functional modules: a CPU that handles high-level control and a dedicated hardware circuit that handles computationally intensive noise filtering operations. The hardware circuit is further segmented into kernel calculation circuits, noise filtering circuits, and blending circuits that operate independently in parallel, achieving high processing speed without overburdening system resources.
Solution Approach 2:
The patent introduces a blending circuit as an intermediary component that combines the outputs of multiple noise filtering circuits. This blending circuit receives de-noised image data from parallel processing paths and merges them into a final output, enabling efficient processing without requiring complex CPU intervention and thus improving productivity while managing device complexity.
Data Source
AI summary
Embodiments relate to an image processing circuit that performs noise reduction on image data. The image processing circuit includes a noise reduction circuit with a kernel calculation circuit, a noise filtering circuit, and a blending circuit. The kernel calculation circuit generates a machine learning (ML) kernel for at least one pixel of an image and a bilateral kernel for the at least one pixel of the image. The noise filtering circuit performs noise filtering of the image using the ML kernel to generate a first de-noised version of the image, and performs noise filtering of the image using the bilateral kernel to generate a second de-noised version of the image. The blending circuit blends each color component of the first de-noised version of the image with a corresponding color component of the second de-noised version of the image to generate a de-noised multi-color version of the image.