Gamma-Corrected Inputs Preserve Quantized Image Restoration Quality
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Solution Overview
Problem
Existing deep learning models for image restoration suffer from significant image quality degradation due to quantization, particularly in low-light environments, as they fail to account for pixel value distributions and illumination conditions, leading to uneven bit allocation and noticeable quality differences.
Innovation Solution
The method involves generating a correction image using a gamma correction value to adjust pixel value distributions based on illumination conditions, followed by inputting this image to a quantized image restoration model and then inversely correcting the output using an inverse gamma correction, thereby optimizing bit allocation and minimizing quality degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If quantization is applied to deep learning models for image restoration, then model complexity and computational requirements are reduced, but image quality deteriorates due to pixel value distribution mismatches
Solution Approach 1:
The patent applies gamma correction to the input image before it enters the quantized deep learning model. This preliminary adjustment of pixel value distribution ensures that the quantized model receives pre-processed data that matches its expected input distribution, thereby maintaining image restoration quality while still benefiting from quantization-induced model simplification
Solution Approach 2:
The patent introduces gamma correction parameters to transform the pixel value distribution of input images. By adjusting these parameters, the system adapts the input data to match the quantized model's expectations, resolving the quality degradation caused by quantization without increasing model complexity
2Productivity
If standard quantization methods are used without input image correction, then processing speed and efficiency are improved, but image quality suffers due to uneven bit allocation across different illumination conditions
Solution Approach 1:
The system performs gamma correction on input images before processing them through the quantized model. This preliminary action ensures that images with different illumination conditions are pre-adjusted to have appropriate pixel value distributions, enabling the quantized model to process them efficiently while maintaining quality
Solution Approach 2:
The patent applies different gamma correction parameters to different regions or characteristics of images based on their illumination conditions. This local adaptation ensures that each image region receives appropriate correction, optimizing bit allocation and preserving image quality across varying lighting scenarios
3Manufacturing precision
If gamma correction is applied to correct pixel value distribution, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent introduces gamma correction as an intermediary processing step between image input and the quantized deep learning model. This intermediary transformation simplifies the overall system by pre-adjusting inputs to match the quantized model's expectations, reducing the need for complex post-processing or model modifications
Data Source
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AI summary
A processor-implemented method with image restoration includes obtaining an input image, generating a correction image by correcting the input image based on a correction parameter that corrects a pixel value distribution of the input image, generating an output image by inputting the correction image to a quantized image restoration model, and generating an enhanced image by inversely correcting the output image based on an inverse correction parameter corresponding to the correction parameter.