Image Display Apparatus Blur Estimation Sub-Area Resolution
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Solution Overview
Problem
Display apparatuses face challenges in maintaining image quality when scaling low-resolution images, as high-frequency area losses lead to blur, particularly at object boundaries, degrading image details.
Innovation Solution
An image display apparatus and method that estimate blur levels in the frequency domain and improve resolution in the spatial domain using models and filters tailored to the estimated blur levels, applying techniques like Fourier transforms, power spectrum analysis, and neural networks to restore image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If a scaler is used to increase the size of a low-resolution image on a high-resolution display apparatus, then the image can be displayed on the display apparatus, but loss occurs in the high frequency area and blur is generated in the boundary portion of objects, degrading image quality
Solution Approach 1:
The image is divided into multiple sub-areas, and blur level estimation and resolution improvement are performed separately for each sub-area. This segmentation allows targeted processing of different image regions with varying blur characteristics, improving overall image quality while managing computational complexity
Solution Approach 2:
Different processing approaches are applied to different sub-areas based on their local blur levels. Sub-areas with high blur levels undergo resolution improvement processing, while sub-areas with low blur levels are processed differently or not at all, optimizing both image quality and processing efficiency
2Manufacturing precision
If resolution improvement processing is applied to the entire image, then image quality can be improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The image is divided into multiple sub-areas, and blur level estimation and resolution improvement are performed separately for each sub-area. This segmentation allows targeted processing of different image regions with varying blur characteristics, improving overall image quality while managing computational complexity
Solution Approach 2:
Resolution improvement processing is applied selectively only to sub-areas with high blur levels rather than the entire image. This partial action approach maintains image quality where needed while significantly reducing processing time and computational resources for sub-areas that don't require intensive processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively removes blur from images by identifying and addressing high-blur areas, enhancing image resolution and maintaining quality across different display resolutions, thereby improving the overall visual clarity of scaled images.
Implementation Method 1
obtain a signal on the frequency domain by performing a Fourier transform on each sub-area of the plurality of sub-areas
Implementation Method 2
obtain a power spectrum for the signal on the frequency domain, and estimate the blur level of each sub-area of the plurality of sub-areas from an inclination of a spectral envelope obtained from the power spectrum
Data Source
AI summary
An image display apparatus includes a display; a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction stored in the memory to: estimate a blur level of each sub-area of a plurality of sub-areas included in a first image; and improve a resolution of at least one sub-area of the plurality of sub-areas, based on an estimated blur level of the at least one sub-area. The display is configured to output a second image including the at least one sub-area having the improved resolution.