Deep Random Projector: Accelerated Deep Image Prior
Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior---encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://github.com/sun-umn/Deep-Random-Projector.
Code (1)
Tasks
DenoisingImage DenoisingImage InpaintingImage RestorationImage Super-ResolutionMeta-LearningSuper-ResolutionSimilar Papers 제목 키워드 기반
GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems
A Generative Adversarial Network (GAN) with generator $G$ trained to model the prior of images has been shown to perform better than sparsity-based regularizers in ill-posed inverse problems. Here, we propose a new metho…
compressed sensingGenerative Adversarial NetworkSuper-ResolutionGhost Projection
Ghost imaging is a developing imaging technique that employs random masks to image a sample. Ghost projection utilizes ghost-imaging concepts to perform the complementary procedure of projection of a desired image. The k…
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting and overfitting to new classes. Existing …
Few-Shot Class-Incremental LearningA Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression
Linear regression is a classic method of data analysis. In recent years, sketching -- a method of dimension reduction using random sampling, random projections, or both -- has gained popularity as an effective computatio…
Dimensionality ReductionregressionEnd-to-end Full Projector Compensation
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface. Traditional methods usually solve the two parts separately and ma…