Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models (2207.14626v2)

Abstract: Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision transformers). Motivated by the recent progress achieved with state-of-the-art conditional generative models, we present a novel patch-based image restoration algorithm based on denoising diffusion probabilistic models. Our patch-based diffusion modeling approach enables size-agnostic image restoration by using a guided denoising process with smoothed noise estimates across overlapping patches during inference. We empirically evaluate our model on benchmark datasets for image desnowing, combined deraining and dehazing, and raindrop removal. We demonstrate our approach to achieve state-of-the-art performances on both weather-specific and multi-weather image restoration, and experimentally show strong generalization to real-world test images.

• The paper presents a novel patch-based denoising diffusion approach that efficiently restores images affected by rain, snow, and haze.
• Empirical evaluations on Snow100K, Outdoor-Rain, and RainDrop datasets demonstrate competitive performance and superior desnowing results.
• The study highlights improved generalization to real-world images, paving the way for robust applications in autonomous systems and surveillance.

Examining Conditional Diffusion Models for Weather-Based Image Restoration

The paper presented by Özan Özdenizci and Robert Legenstein addresses the application of denoising diffusion probabilistic models to image restoration under adverse weather conditions. This paper is notable in its exploration of how conditional generative models can be leveraged for practical use in computer vision tasks, specifically targeting the restoration of images influenced by severe weather effects such as rain, snow, and haze.

The authors introduce a novel approach that uses patch-based image restoration algorithms driven by denoising diffusion models. This patch-based method circumvents architectural limitations that typically restrict the size adaptability of generative models, allowing for efficient processing of images of varying dimensions. This capability is critical given the diverse size distribution of images encountered in computer vision datasets and real-world scenarios.

Empirical evaluations showcase the model's performance across several benchmark datasets, which include Snow100K for desnowing tasks, Outdoor-Rain for combined deraining and dehazing tasks, and RainDrop for raindrop removal. The paper provides quantitative results indicating that the proposed patch-based diffusion models maintain competitive performance, achieving state-of-the-art results particularly in the image desnowing domain, surpassing leading techniques such as DDMSNet and DesnowNet.

One of the paper's key claims is the improved generalization capability to real-world images, demonstrating robustness in synthetic-to-real transitions via perceptual quality assessments. This has meaningful implications for the deployment of such technology in autonomous systems and enhancing visibility in automated surveillance.

The theoretical implications of integrating conditional diffusion models in image restoration involve a promising avenue for further exploration within generative model frameworks. The conditional aspect enables specific adaptability to diverse weather conditions, framing a pathway towards unified multi-condition restoration architectures, which are inherently versatile given the ill-posed nature of restoration problems.

Potential future directions of research could explore optimization strategies that minimize the computational demand typical of current generative models. This would involve refining model architectures, incorporating adaptive patch-processing strategies, and potentially leveraging parallel processing methodologies to further expedite inference times. Taking inspirations from recent advancements in diffusion model optimizations such as guided score-based estimation and classifier-free guidance could yield notable performance improvements.

In summary, the work by Özdenizci and Legenstein provides an insightful contribution to the field of image restoration, articulating the efficacy of patch-based diffusion models in tackling complex adverse weather conditions. Such innovations hold the promise of enhancing computer vision applications where environmental factors heavily impact image quality, thus further enriching machine perception in real-world settings.