Kernel Offset Adjustment for Image Contrast Restoration
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Solution Overview
Problem
Existing image processing technologies face challenges in accurately restoring original images from degraded ones due to unpredictable noise, incomplete modeling, and insufficient prior information, leading to inadequate contrast restoration and computational burdens.
Innovation Solution
The method involves setting an offset window and parameter for a kernel offset, applying it to an input kernel to generate an output kernel with improved contrast adjustment, and normalizing the kernel intensity to enhance image restoration, specifically by adjusting kernel size and offset pattern based on image characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing image processing technologies are used for image restoration, then the restoration process can be performed, but the contrast restoration is inadequate and image quality is degraded due to unpredictable noise and incomplete modeling
Solution Approach 1:
The patent modifies the degradation kernel by applying a kernel offset with specific distribution characteristics (Gaussian distribution with controlled standard deviation) to adjust the restoration parameters. This parameter change enables better contrast restoration by transforming the kernel's frequency response characteristics, allowing the system to recover fine details while suppressing noise effectively
Solution Approach 2:
The patent introduces an intermediate kernel transformation process that acts as a mediator between the degraded image and the final restored image. By applying the offset kernel to modify the degradation kernel first, the system creates an optimized restoration kernel that bridges the gap between incomplete modeling and accurate restoration, improving both contrast and overall image quality
2Manufacturing precision
If kernel size is increased to improve restoration performance, then more image characteristics can be captured, but computational burden increases
Solution Approach 1:
The patent applies different kernel offsets to different regions of the image based on local characteristics. By detecting edges and distinguishing between edge regions and non-edge regions, the system applies targeted kernel modifications only where needed, rather than uniformly processing the entire image. This local approach maintains high restoration performance at edges while reducing unnecessary computations in smooth regions
3Ease of operation
If uniform kernel processing is applied to the entire image, then the process is simple, but image characteristics in different areas are not adequately addressed
Solution Approach 1:
The patent implements region-adaptive kernel processing by detecting edges and dividing the image into edge regions and non-edge regions. Different kernel offsets are applied to each region type: edge regions receive kernels optimized for preserving sharp transitions, while non-edge regions receive kernels optimized for smooth restoration. This local differentiation maintains processing simplicity through automated region classification while achieving adaptability to various image characteristics
Solution Approach 2:
The patent dynamically adjusts the kernel offset parameters based on local image characteristics detected in real-time. The system evaluates edge strength and orientation at each location and adapts the kernel properties accordingly, allowing the restoration process to respond dynamically to varying image content rather than applying a static uniform kernel throughout
Data Source
AI summary
A method with image processing includes: setting an offset window for an offset pattern of a kernel offset and an offset parameter for an application intensity of the kernel offset; determining an output kernel by applying the kernel offset to an input kernel based on the offset window and the offset parameter; and adjusting contrast of a degraded image using the output kernel.