Image Signal Processor Noise Reduction via Pixel Binning Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image signal processing methods face challenges in reducing noise in color filter array images while maintaining image resolution, especially in low-light environments, and often result in distortion or loss of texture detail.
Innovation Solution
An image signal processing method that utilizes pixel binning to compute specific information from both original and pixel binned CFA images, allowing for noise reduction without losing resolution, by integrating pixel binning techniques into the digital image signal processing pipeline, which includes preprocessing, Bayer domain de-noising, color interpolation, and RGB domain de-noising units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If pixel binning is applied to reduce readout noise, then noise level is reduced, but image resolution deteriorates
Solution Approach 1:
The patent divides the image processing into two separate streams: one processing the original CFA image at full resolution and another processing the pixel-binned CFA image for noise reduction. By segmenting the processing paths and selectively combining results, the patent maintains full resolution while incorporating noise reduction benefits from pixel binning.
Solution Approach 2:
The patent introduces an intermediary processing step where specific information (noise characteristics, statistical parameters) is extracted from the pixel-binned image and used to guide the processing of the original image. This intermediary information acts as a mediator that transfers noise reduction benefits without requiring direct use of the downsampled binned image.
2Object-affected harmful factors
If de-noising processes are applied in Bayer domain, then noise is reduced, but image distortion and loss of texture detail occur
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local characteristics. By analyzing local variance and edge information, the patent selectively applies de-noising strength, preserving texture details in high-frequency regions while reducing noise in smooth areas, thus achieving local optimization of noise reduction without uniform loss of detail.
Solution Approach 2:
The patent employs dynamic adjustment of de-noising parameters based on local image characteristics such as edge detection results and variance measurements. The de-noising strength is adaptively modulated across different regions and scales, allowing the system to dynamically preserve important texture information while removing noise, rather than applying a static de-noising filter throughout.
3Object-affected harmful factors
If weighted means are used to adjust luminance and chrominance values, then noise floor is reduced, but line buffer resources are consumed and may cause distortion
Solution Approach 1:
The patent performs preliminary extraction of specific information (noise statistics, variance, edge characteristics) from the pixel-binned image before processing the original image. By pre-computing these guiding parameters from the downsampled image, the patent avoids the need for extensive line buffer resources during the main de-noising process, as the heavy statistical analysis is already completed in advance.
Data Source
AI summary
An image signal processing method includes: receiving an original color filter array (CFA) image and a pixel binned CFA image; computing a specific information of the pixel binned CFA image; and processing the original CFA image according to the specific information. The associated image signal processor includes an input terminal, an operating unit and a processing unit, wherein the input terminal is for receiving an original CFA image and a pixel binned CFA image, the operating unit is for computing a specific information of the pixel binned CFA image, and the processing unit is for processing the original CFA image according to the specific information and utilizing the pixel binned CFA image.