DeepLPF: Deep Local Parametric Filters for Image Enhancement (2003.13985v1)

Abstract: Digital artists often improve the aesthetic quality of digital photographs through manual retouching. Beyond global adjustments, professional image editing programs provide local adjustment tools operating on specific parts of an image. Options include parametric (graduated, radial filters) and unconstrained brush tools. These highly expressive tools enable a diverse set of local image enhancements. However, their use can be time consuming, and requires artistic capability. State-of-the-art automated image enhancement approaches typically focus on learning pixel-level or global enhancements. The former can be noisy and lack interpretability, while the latter can fail to capture fine-grained adjustments. In this paper, we introduce a novel approach to automatically enhance images using learned spatially local filters of three different types (Elliptical Filter, Graduated Filter, Polynomial Filter). We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image. DeepLPF provides a natural form of model regularization and enables interpretable, intuitive adjustments that lead to visually pleasing results. We report on multiple benchmarks and show that DeepLPF produces state-of-the-art performance on two variants of the MIT-Adobe-5K dataset, often using a fraction of the parameters required for competing methods.

• The paper introduces a novel approach using three local parametric filters integrated within a U-Net to enhance images with precision and efficiency.
• It employs Elliptical, Graduated, and Polynomial Filters to mimic manual editing tools, achieving significant improvements in PSNR, SSIM, and LPIPS metrics.
• The interpretable design bridges manual and automated enhancements, offering robust performance on datasets like MIT-Adobe 5K and SID while reducing model complexity.

DeepLPF: Deep Local Parametric Filters for Image Enhancement

The paper "DeepLPF: Deep Local Parametric Filters for Image Enhancement" addresses the challenge of enhancing digital photographs using automated methodologies. Traditional approaches typically involve either pixel-level or global adjustments, each with inherent limitations related to noise and failure in capturing fine-grained details. DeepLPF proposes a novel approach integrating the concept of local parametric filters inspired by manual editing tools used in professional image editing software.

Overview of Methodology

DeepLPF introduces three types of spatially localized filters for image enhancement: Elliptical Filters, Graduated Filters, and Polynomial Filters. These filters are analogous to parametric tools available in software like Adobe Lightroom or Photoshop, which allow for specified regional edits within an image.

1. Elliptical Filters are used to adjust specific regions, often applied where focal items like faces require enhancement.
2. Graduated Filters are employed to enhance areas with gradient characteristics such as skies, using linear adjustments.
3. Polynomial Filters allow for smooth pixel-level adjustments across an image, effectively simulating a brush tool.

The DeepLPF framework incorporates a neural network architecture that regresses the parameters for these filters. A U-Net backbone is utilized to both estimate a feature map and facilitate the regression of filter parameters. The output is a fused image enhancement accomplished through the learned application of these parametric filters.

Numerical Results and Evaluation

The paper demonstrates the capability of DeepLPF to outperform state-of-the-art methods on recognized benchmarks such as MIT-Adobe 5k, with reduced model parameter requirements. For instance, the DeepLPF model achieves significant improvements in PSNR, SSIM, and LPIPS metrics in comparison to competing approaches. Notably, the results on MIT-Adobe-5K-DPE dataset reflect competitive performance with about half the parameter count of the leading models.

Moreover, the approach has been assessed on multiple datasets including MIT-Adobe-5K-UPE and the challenging See-in-the-dark (SID) dataset. On all tested benchmarks, DeepLPF quantitatively outperformed previous methods, providing robust image enhancement across varied scenarios.

Implications and Future Developments

The interpretable nature of local parametric filters offers a significant advantage by aligning automated enhancements with familiar manual editing practices, making results intuitive and user-friendly. This regularizes the model, mitigating overfitting and ensuring weight efficiency. The success of this approach suggests several interesting directions for further investigation:

• Extension to More Filters and Tools: Integrating additional parametric controls that expand beyond elliptical and graduated filters could offer greater flexibility and refinement, including edge-preserving and texture-specific enhancements.
• Dynamic Enhancement Sequences: Employing techniques like reinforcement learning to dynamically determine sequences of filter application could optimize the enhancement process further.
• Customization and User Interaction: Allowing user-driven customizations with learned models could balance automated efficiency and personal aesthetic preferences.

The contributions of this research extend both theoretically and practically to the field of automated image enhancement, holding the potential to streamline professional workflows and democratize image quality improvement for non-experts. This paper successfully addresses the gap between manual and automated image editing, proposing an efficient, accurate, and user-aligned solution.