CNN Image Enhancement Model Using Segmented Loss Functions
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Solution Overview
Problem
Existing image enhancement methods face issues such as over-blurring and over-smoothing due to loss functions that fail to adequately consider multiple features and relationships between pixels, leading to suboptimal image processing outcomes.
Innovation Solution
A method involving the training of convolutional neural network (CNN) structure models using constraint features like Sobel, Prewitt, contourlet transform, and gradient features, with a loss function determined by weighting CNN models based on their contribution and order of magnitude, allowing for the establishment of an image enhancement model that adjusts parameter values to optimize image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manually designed loss functions are used for image enhancement, then the model structure remains simple, but the image quality deteriorates due to over-blurring and over-smoothing
Solution Approach 1:
The loss function is segmented into multiple independent components, each targeting a specific image feature (edge preservation, gradient consistency, contour accuracy). This allows each component to optimize a particular aspect without compromising overall image quality, resolving the contradiction between simple structure and high quality by dividing the complex quality control into manageable segments.
Solution Approach 2:
The invention introduces multiple learnable parameter sets (weighting factors for different loss components, scaling parameters for gradient and contour features) that dynamically adjust the optimization process. These parameter changes enable the model to balance between preserving edges and smoothing regions, achieving high image quality without requiring complex model architecture.
2Manufacturing precision
If multiple constraint features are incorporated into the loss function, then image quality improves, but the computational complexity increases
Solution Approach 1:
The invention computes constraint features (edges, gradients, contours) as preliminary intermediate representations during the forward pass. These pre-computed features are then reused across multiple loss function components, avoiding redundant calculations. This preliminary action reduces computational complexity while maintaining the benefits of multiple constraint features for image quality.
Solution Approach 2:
The extracted constraint features serve multiple functions simultaneously: edges are used for both edge preservation loss and gradient consistency loss, contours are used for both contour accuracy loss and structure preservation. This multi-functionality reduces the total computational burden while achieving comprehensive image quality improvement through multiple constraints.
3Productivity
If traditional loss functions are used, then training is faster, but image details are lost due to over-smoothing
Solution Approach 1:
The loss function incorporates feedback mechanisms through gradient-based terms that explicitly measure and penalize information loss. The gradient consistency loss and contour accuracy loss provide continuous feedback during training about detail preservation, guiding the model to maintain image details while still achieving fast convergence through efficient gradient computation.
Solution Approach 2:
The invention uses learnable scaling parameters and weighting factors that adapt during training to balance convergence speed and detail preservation. These parameter changes allow the model to prioritize different loss components at different training stages, achieving both fast training and detailed output by dynamically adjusting the optimization focus.
Data Source
AI summary
Methods, devices, systems and apparatus for training image enhancement models and enhancing images are provided. In one aspect, a method of training an image enhancement model includes: for each of one or more constraint features, processing a ground truth image with the constraint feature to obtain a feature image corresponding to the constraint feature, for each of the one or more feature images, using the ground truth image and the feature image to train a convolutional neural network (CNN) structure model corresponding to the feature image, determining a loss function of the image enhancement model based on the one or more CNN structure models corresponding to the one or more feature images, and establishing the image enhancement model based on the loss function. A to-be-enhanced image can be input into the established image enhancement model to obtain an enhanced image.