Neural Network Image Deblurring via Multi-Scale Training
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Solution Overview
Problem
Traditional image deblurring methods rely on strict convolution model hypotheses that are rarely satisfied in complex scenarios with camera and object movement, leading to poor deblurring effects and dependability issues.
Innovation Solution
An image processing method that trains a target model using sample images of different scales, composed of clear images blurred to represent actual scenarios, allowing for effective deblurring by iteratively refining the model through neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative deblurring methods with fixed convolution kernel models are used, then the method is simple to implement, but the deblurring effect is poor in complex scenarios with camera and object movement
Solution Approach 1:
The patent transforms the fixed convolution kernel model into a dynamic neural network model that can adapt to different blurring scenarios. The neural network learns optimal deblurring parameters dynamically based on input image characteristics, enabling effective deblurring in complex scenarios with camera and object movement while maintaining reasonable computational complexity through efficient network architecture design.
Solution Approach 2:
The patent changes the parameters of the deblurring model from fixed convolution kernels to learnable neural network parameters. By training the neural network on diverse blurred images, the model parameters are optimized to handle various blurring conditions, significantly improving deblurring reliability in complex scenarios compared to traditional fixed-kernel methods.
2Extent of automation
If neural network methods with convolution models are used, then automated deblurring is achieved, but the strict convolution model hypothesis is rarely satisfied in actual blurred image scenarios
Solution Approach 1:
The patent employs a dynamic neural network model that adapts to different blurring scenarios rather than relying on strict convolution model hypotheses. The network learns to handle various motion patterns and blurring conditions through training, maintaining high automation while improving dependability in actual complex scenarios with camera and object movement.
Solution Approach 2:
The patent transforms the rigid convolution model parameters into flexible learnable parameters through neural network training. This allows the model to automatically adjust to different blurring conditions without relying on strict theoretical hypotheses, thereby maintaining automation while significantly improving deblurring dependability in real-world scenarios.
3Reliability
If continuous iteration of convolution kernel estimation and image deconvolution is performed, then deblurring effect is gradually optimized, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary training of the neural network model offline using大量 blurred and clear image pairs. This preliminary action pre-optimizes the deblurring parameters, so that during actual application, the pre-trained model can quickly process images without requiring extensive iterative computation, significantly reducing processing time while maintaining high deblurring quality.
Solution Approach 2:
The patent uses a pre-trained neural network model that has learned optimal deblurring patterns from training data. Instead of performing continuous iterative optimization for each image, the system copies the learned knowledge from the trained model to rapidly process new images, achieving both high deblurring quality and fast processing speed.
Data Source
AI summary
An image processing method and device, storage medium and electronic device for deblurring an image. The method includes obtaining an image processing instruction including an instruction to deblur a target blurred image; obtaining a target model by training an original model based on a plurality of sample images of different scales, one of the plurality of sample images being a blurred image composed of a plurality of clear images, and the obtained target model being used for deblurring the blurred image to obtain a clear image; based on the image processing instruction, using the target model to deblur the target blurred image to obtain a target clear image; and outputting the target clear image.