# Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling

arXiv: https://arxiv.org/abs/1910.08242 · HTML: https://mlsanity.com/paper/1910.08242/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2019.

## Built on by (most cited first, 14 of 14)

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- [Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training](https://mlsanity.com/paper/2206.07875/) (2022)
- [Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining](https://mlsanity.com/paper/2305.18092/) (2023)
- [Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion](https://mlsanity.com/paper/2404.06033/) (2024)
- [Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation](https://mlsanity.com/paper/2406.02064/) (2024)
- [Transmission and Color-guided Network for Underwater Image Enhancement](https://mlsanity.com/paper/2308.04892/) (2023)
- [Histogram-guided Video Colorization Structure with Spatial-Temporal Connection](https://mlsanity.com/paper/2308.04899/) (2023)
- [Towards General and Fast Video Derain via Knowledge Distillation](https://mlsanity.com/paper/2308.05346/) (2023)

## Abstract (arXiv)

Properly modeling latent image distributions plays an important role in a variety of image-related vision problems. Most exiting approaches aim to formulate this problem as optimization models (e.g., Maximum A Posterior, MAP) with handcrafted priors. In recent years, different CNN modules are also considered as deep priors to regularize the image modeling process. However, these explicit regularization techniques require deep understandings on the problem and elaborately mathematical skills. In this work, we provide a new perspective, named Task-driven Latent Feasibility (TLF), to incorporate specific task information to narrow down the solution space for the optimization-based image modeling problem. Thanks to the flexibility of TLF, both designed and trained constraints can be embedded into the optimization process. By introducing control mechanisms based on the monotonicity and boundedness conditions, we can also strictly prove the convergence of our proposed inference process. We demonstrate that different types of image modeling problems, such as image deblurring and rain streaks removals, can all be appropriately addressed within our TLF framework. Extensive experiments also verify the theoretical results and show the advantages of our method against existing state-of-the-art approaches.