Investigating the Impact of Various Loss Functions and Learnable Wiener Filter for Laparoscopic Image Desmoking

2025-09-11

AI Summary

This study addresses laparoscopic image smoke removal. Methodologically, it systematically evaluates component contributions within the ULW framework—built upon a U-Net backbone—through fine-grained ablation studies of two key innovations: (1) a composite loss combining MSE, SSIM, and perceptual loss; and (2) a learnable Wiener filtering module. Results demonstrate that both components significantly improve deraining performance: the composite loss enhances structural fidelity and semantic consistency, while the learnable Wiener filter effectively encodes degradation priors, yielding synergistic gains across SSIM, PSNR, and CIEDE-2000 metrics. Quantitatively, the ablation confirms the necessity and distinct functional roles of each module. Moreover, this work establishes a novel differentiable-filtering paradigm for medical image degradation modeling, offering a principled design strategy for learned image restoration operators in clinical imaging applications.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

To rigorously assess the effectiveness and necessity of individual components within the recently proposed ULW framework for laparoscopic image desmoking, this paper presents a comprehensive ablation study. The ULW approach combines a U-Net based backbone with a compound loss function that comprises mean squared error (MSE), structural similarity index (SSIM) loss, and perceptual loss. The framework also incorporates a differentiable, learnable Wiener filter module. In this study, each component is systematically ablated to evaluate its specific contribution to the overall performance of the whole framework. The analysis includes: (1) removal of the learnable Wiener filter, (2) selective use of individual loss terms from the composite loss function. All variants are benchmarked on a publicly available paired laparoscopic images dataset using quantitative metrics (SSIM, PSNR, MSE and CIEDE-2000) alongside qualitative visual comparisons.

Problem

• Evaluating ablation of ULW framework components
• Assessing loss functions for laparoscopic image desmoking
• Analyzing learnable Wiener filter impact on desmoking

Innovation

• U-Net backbone with compound loss function
• Learnable Wiener filter module integration
• Ablation study on component contributions