Image Restoration using Total Variation Regularized Deep Image Prior (1810.12864v1)

Abstract: In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring.

• The paper introduces DIP-TV, a novel image restoration method combining Deep Image Prior (DIP) with Total Variation (TV) regularization to enhance image quality without requiring large training datasets.
• DIP-TV demonstrates superior performance over traditional methods like BM3D and standalone DIP in image denoising (e.g., ~0.5 dB SNR improvement) and competitive results in deblurring tasks.
• This approach highlights the potential of fusing traditional analytical regularization techniques with modern deep learning architectures for addressing ill-posed image reconstruction problems, particularly when training data is limited.

Image Restoration Using Total Variation Regularized Deep Image Prior

The paper "Image Restoration using Total Variation Regularized Deep Image Prior" presents an advanced methodology for image restoration by integrating the deep image prior (DIP) framework with total variation (TV) regularization. Image reconstruction is a quintessential task in computational imaging, often confronted with the challenges of expensive calculation and noise interference. Traditional approaches using sparsity-driven regularization, such as the TV, have demonstrated effectiveness in constraining solutions to fit analytical models of sparsity. However, advancements in deep learning have prompted a shift towards trainable models, enhancing the capability to recover images from noisy or incomplete data.

Methodological Framework

The paper innovates on the DIP framework, a non-data-driven regularization technique, by introducing a hybrid model known as DIP-TV. The DIP framework inherently utilizes a convolutional neural network (CNN) architecture to implicitly regularize image reconstruction without relying on large datasets for training. The intuition behind DIP is based on the observation that CNNs can effectively represent natural images while inherently filtering out noise and distortions.

The integration of TV regularization into the DIP framework is a significant methodological enhancement proposed by the authors. TV regularization is used to promote sparsity in image gradients, effectively constraining the solutions synthesized by CNN to ensure piecewise smoothness. In this paper, the authors combine the implicit CNN regularization and explicit TV penalty, aiming to enhance image quality by preserving the homogeneity of background and detailed textures.

Experimental Results and Performance

The authors undertook a comprehensive set of experiments, demonstrating the efficacy of DIP-TV in addressing image denoising and deblurring tasks. For image denoising, DIP-TV has shown superior performance when compared to established methods such as BM3D and EPLL in scenarios of high noise levels. Specifically, it outperformed traditional DIP by approximately 0.5 dB in SNR across a range of noise levels from 5 dB to 20 dB. Importantly, these results position DIP-TV as a competitive approach to recent state-of-the-art techniques without requiring extensive training on pre-existing image datasets.

In terms of image deblurring, DIP-TV exhibited impressive PSNR results, especially with complex blur kernels and varying levels of noise interference. The proposed approach has been shown to be particularly adept at reconstructing images that are severely degraded, thanks to its ability to efficiently balance CNN model-based reconstruction and TV regularization. The paper notes that DIP-TV’s improvements in PSNR are substantial, exceeding those achieved by standalone DIP methods and nearly matching the performance of leading alternatives such as IRCNN.

Implications and Future Directions

The integration of TV regularization into the DIP framework opens new avenues for image reconstruction technologies, allowing for improved performance in ill-posed image restoration scenarios. Since DIP-TV does not require data-driven training, it holds significant potential for applications where training data is sparse or unavailable.

This research advances the field by demonstrating how traditional regularization techniques can be effectively combined with modern deep learning structures for enhanced image quality. Future developments may include exploring other regularization techniques with DIP, extending the DIP-TV methodology to other forms of image degradation beyond noise and blur, and optimizing the CNN architecture for various image modalities.

In conclusion, the findings from this paper contribute valuable insights into the fusion of analytical and data-driven methodologies for computational imaging, setting a promising foundation for further innovations in image restoration technologies.